# ENSEMBL DATABASE
# v102 is for the latest mm10 version
# ensembl.v102 <- useMart(host = "https://nov2020.archive.ensembl.org",
# biomart = "ENSEMBL_MART_ENSEMBL",
# dataset = "mmusculus_gene_ensembl")palette_1 = list(red = "#E9002D",
amber = "#FFAA00",
green = "#00B000")
palette_2 = list(red = "#FF1F5B",
green = "#00CD6C",
blue = "#009ADE",
purple = "#AF58BA",
yellow = "#FFC61E",
orange = "#F28522",
grey = "#A0B1BA",
brown = "#A6761D")
palette_3 = list(grey1 = "#a0b1ba",
grey2 = "#c5d0d5",
grey3 = "#eceff1")
colorListLoop <- c(palette_3[["grey3"]], palette_3[["grey2"]], palette_3[["grey1"]],
"#C5E1EF", "#6CB0D6", "#226E9C",
"#06592A",
"#FED976", "#FD8D3C", "#E31A1C")
colorListPromoter <- c(palette_3[["grey3"]],"#E88587", "#E31A1C")
colorListEnhancer <- c(palette_3[["grey3"]],"#87AFC7", "#226E9C")
colorListStructure <- c(palette_3[["grey3"]], palette_3[["grey2"]], palette_3[["grey1"]])refDir <- here("reference")library(colorspace)
fontType <- "Helvetica"
fontSizeL <- 10 # pt
fontSizeM <- 8
fontSizeS <- 6
lineThick <- 0.75 # pt
lineMedium <- 0.5
lineThin <- 0.25
panelUnit <- 30 # mm
panelMargin <- 1.5
mmToInch <- 0.03937007874
mmToLineUnit <- 1/2.13
mmToLinePlotgarden <- 1/0.75
ptToMM <- 1/2.845
strong_red <- "#CB333A"
strong_blue <- "#4851A0"
weak_red <- lighten(strong_red, amount = 0.4) # FF7D81
weak_blue <- lighten(strong_blue, amount = 0.4) # 8A91DD
no_grey <- "#A8A8A8"
strong_teel <- "#0892A5"
strong_green <- "#23CE6B" # A485
strong_darkgreen <- "#054A29"
strong_yellow <- "#FFBA49"
strong_orange <- "#F18F01" # dTAG
strong_lightpurple <- "#BD93D8"
strong_purple <- "#9E33CB" # Epi
panelSize <- function(num, unit = panelUnit, margin = panelMargin){
return(num*unit - 2*margin)
}
importBedpe = function(bedpe){
a1 = makeGRangesFromDataFrame(data.frame(
chr = bedpe$V1,
start = bedpe$V2 +1,
end = bedpe$V3))
a2 = makeGRangesFromDataFrame(data.frame(
chr = bedpe$V4,
start = bedpe$V5 +1,
end = bedpe$V6))
GInteractions(a1, a2)
}
get_density <- function(x, y, ...) {
dens <- MASS::kde2d(x, y, ...)
ix <- findInterval(x, dens$x)
iy <- findInterval(y, dens$y)
ii <- cbind(ix, iy)
return(dens$z[ii])
}
label_kb_mb <- function(x) {
ifelse(x >= 1000000, paste0(x / 1000000, "Mb"), paste0(x / 1000, "kb"))
}
importPeak = function(fileName){
df = fread(fileName)
gr = makeGRangesFromDataFrame(data.frame(
chr = df$V1, start = df$V2, end = df$V3
))
}Loop should be dropped from specific comparison if ooe in any of the condition are NA If ooe is 0, fixed pseudo value of 0.01 will be used. log2(ooe) should be plotted in range (-5, 8) if plotted in scatterplot ### Common stuffs
# Parameters
commonLoopDir <- here("../data/loop_analysis")
# Defining gene neighborhood
flankSize <- 2500
gene.TSS.tb <- fread(here(refDir, "mm10", "mm10_GRCm38.p6_gene_sorted.bed")) %>%
dplyr::mutate(TSS = ifelse(V4 == "+", V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)
colnames(gene.TSS.tb) <- c("chr", "start", "end", "strand", "gene", "ensembl")
fwrite(gene.TSS.tb, here(refDir, "mm10", "mm10_GRCm38.p6_TSS2.5kb.bed"), sep = "\t", col.names = FALSE, row.names = FALSE)
# Functions for calculating gene overlap
findOverlapGene <- function(gene.TSS.tb, chrom1, start1, end1){
temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%
dplyr::filter((start <= end1) & (end >= start1))
return(temp$gene)
}
findOverlapEnsembl <- function(gene.TSS.tb, chrom1, start1, end1){
temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%
dplyr::filter((start <= end1) & (end >= start1))
return(temp$ensembl)
}################################################################################
# Import annotated postprocessed loops with chromosight score
loopDir <- here("../data/loop_chromosight")
data <- as_tibble(fread(here(loopDir, "chromo_union_allRes_postprocessed_annotated.tsv")))
################################################################################
# Integrate chromosight score from asynchronous experiment
resolutions <- c(25000, 10000, 5000)
# helper: read, filter, select and rename for one experiment
annotate_scores <- function(experiment, suffix) {
map_dfr(resolutions, function(res) {
fread(here(loopDir,
sprintf("GSE178982_%s_pooled_union_%dbp_pu100pz100.tsv",
experiment, res))
) %>%
mutate(id = paste(chrom1, start1, end1, chrom2, start2, end2, sep = "_")) %>%
filter(start2 - start1 <= 2e6) %>%
select(id, score, pvalue, qvalue) %>%
rename_with(~ paste0(., "_", suffix),
c(score, pvalue, qvalue))
})
}
data <- data %>%
left_join(annotate_scores("AsyncUT", "UT"), by = "id")
data <- data %>%
left_join(annotate_scores("AsyncAID", "AID"), by = "id")
data <- data %>%
relocate(score_UT:qvalue_AID, .after = qvalue_EpidTAG)
data <- data %>% dplyr::select(-seq(29, 41))
data <- data %>% relocate(A1:Anno_simple, .after = id)
################################################################################
# Adding obs/exp
import_obsexp <- function(experiment, suffix) {
map_dfr(resolutions, function(res) {
fread(here(loopDir,
sprintf("chromo_union_allRes_postprocessed_%dbp_obxexp_%s_pooled.tsv",
res, experiment))
) %>%
mutate(id = paste(chrom1, start1, end1, chrom2, start2, end2, sep = "_")) %>%
select(id, ooe) %>%
rename_with(~ paste0(., "_", suffix),
c(ooe))
})
}
data <- data %>%
left_join(import_obsexp("G1DMSO", "DMSO"), by = "id")
data <- data %>%
left_join(import_obsexp("G1dTAG", "dTAG"), by = "id")
data <- data %>%
left_join(import_obsexp("G1A485", "A485"), by = "id")
data <- data %>%
left_join(import_obsexp("EpiG1DMSO", "EpiDMSO"), by = "id")
data <- data %>%
left_join(import_obsexp("EpiG1dTAG", "EpidTAG"), by = "id")
data <- data %>%
left_join(import_obsexp("GSE178982_AsyncUT", "UT"), by = "id")
data <- data %>%
left_join(import_obsexp("GSE178982_AsyncAID", "AID"), by = "id")
fwrite(data, here(commonLoopDir, "loopScore_chromosight.tsv"),
sep = "\t", col.names = TRUE)
################################################################################
# Annotate genes for p-n loops
name <- "chromosight"
gene.TSS.tb <- fread(here(refDir, "mm10", "mm10_GRCm38.p6_TSS2.5kb.bed"))
colnames(gene.TSS.tb) <- c("chr", "start", "end", "strand", "gene", "ensembl")
# Simple annotation
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1_simple == "P",
list(findOverlapGene(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2_simple == "P",
list(findOverlapGene(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(Anno_simple %in% c("P-P", "P-E", "P-S", "P-X")) %>% dplyr::select(-c("A1_gene", "A2_gene"))
fwrite(temp_p_n, here(commonLoopDir, paste0("loopScore_", name, "_p-n_simple_geneList.tsv")), sep = "\t")
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1_simple == "P",
list(findOverlapEnsembl(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2_simple == "P",
list(findOverlapEnsembl(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(Anno_simple %in% c("P-P", "P-E", "P-S", "P-X")) %>% dplyr::select(-c("A1_gene", "A2_gene"))
fwrite(temp_p_n, here(commonLoopDir, paste0("loopScore_", name, "_p-n_simple_ensemblList.tsv")), sep = "\t")
# complex annotation
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1 %in% c("P", "SP"),
list(findOverlapGene(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2 %in% c("P", "SP"),
list(findOverlapGene(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(str_detect(Anno, "P")) %>%
dplyr::select(-c("A1_gene", "A2_gene"))
fwrite(temp_p_n, here(commonLoopDir, paste0("loopScore_", name, "_p-n_complex_geneList.tsv")), sep = "\t")
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1 %in% c("P", "SP"),
list(findOverlapEnsembl(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2 %in% c("P", "SP"),
list(findOverlapEnsembl(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(str_detect(Anno, "P")) %>%
dplyr::select(-c("A1_gene", "A2_gene"))
fwrite(temp_p_n, here(commonLoopDir, paste0("loopScore_", name, "_p-n_complex_ensemblList.tsv")), sep = "\t")
################################################################################
loopDir <- here("../data/loop_hicdcp")
################################################################################
# Import annotated postprocessed loops with chromosight score
data <- as_tibble(fread(here(loopDir, "hicdcp_union_10000bp_annotated.tsv"))) %>%
mutate(id = paste(chrom1, start1, end1, chrom2, start2, end2, sep = "_")) %>%
relocate(id, .after = end2) %>%
dplyr::select(-c("anchor_id_1", "anchor_id_2"))
resolutions <- c(10000)
# Adding obs/exp
import_obsexp <- function(experiment, suffix) {
map_dfr(resolutions, function(res) {
fread(here(loopDir,
sprintf("hicdcp_union_%dbp_obxexp_%s_pooled.tsv",
res, experiment))
) %>%
mutate(id = paste(chrom1, start1, end1, chrom2, start2, end2, sep = "_")) %>%
select(id, ooe) %>%
rename_with(~ paste0(., "_", suffix),
c(ooe))
})
}
data <- data %>%
left_join(import_obsexp("G1DMSO", "DMSO"), by = "id")
data <- data %>%
left_join(import_obsexp("G1dTAG", "dTAG"), by = "id")
data <- data %>%
left_join(import_obsexp("G1A485", "A485"), by = "id")
data <- data %>%
left_join(import_obsexp("EpiG1DMSO", "EpiDMSO"), by = "id")
data <- data %>%
left_join(import_obsexp("EpiG1dTAG", "EpidTAG"), by = "id")
data <- data %>%
left_join(import_obsexp("GSE178982_AsyncUT", "UT"), by = "id")
data <- data %>%
left_join(import_obsexp("GSE178982_AsyncAID", "AID"), by = "id")
fwrite(data, here(commonLoopDir, "loopScore_hicdcp.tsv"),
sep = "\t", col.names = TRUE)
################################################################################
# Annotate genes for p-n loops
name <- "hicdcp"
gene.TSS.tb <- fread(here(refDir, "mm10", "mm10_GRCm38.p6_TSS2.5kb.bed"))
colnames(gene.TSS.tb) <- c("chr", "start", "end", "strand", "gene", "ensembl")
# Simple annotation
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1_simple == "P",
list(findOverlapGene(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2_simple == "P",
list(findOverlapGene(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(Anno_simple %in% c("P-P", "P-E", "P-S", "P-X")) %>% dplyr::select(-c("A1_gene", "A2_gene"))
fwrite(temp_p_n, here(commonLoopDir, paste0("loopScore_", name, "_p-n_simple_geneList.tsv")), sep = "\t")
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1_simple == "P",
list(findOverlapEnsembl(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2_simple == "P",
list(findOverlapEnsembl(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(Anno_simple %in% c("P-P", "P-E", "P-S", "P-X")) %>% dplyr::select(-c("A1_gene", "A2_gene"))
fwrite(temp_p_n, here(commonLoopDir, paste0("loopScore_", name, "_p-n_simple_ensemblList.tsv")), sep = "\t")
# complex annotation
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1 %in% c("P", "SP"),
list(findOverlapGene(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2 %in% c("P", "SP"),
list(findOverlapGene(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(str_detect(Anno, "P")) %>%
dplyr::select(-c("A1_gene", "A2_gene"))
fwrite(temp_p_n, here(commonLoopDir, paste0("loopScore_", name, "_p-n_complex_geneList.tsv")), sep = "\t")
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1 %in% c("P", "SP"),
list(findOverlapEnsembl(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2 %in% c("P", "SP"),
list(findOverlapEnsembl(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(str_detect(Anno, "P")) %>%
dplyr::select(-c("A1_gene", "A2_gene"))
fwrite(temp_p_n, here(commonLoopDir, paste0("loopScore_", name, "_p-n_complex_ensemblList.tsv")), sep = "\t")
Loops with
<!-- rnb-source-begin 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 -->
```r
loop_chromo <- as_tibble(fread(here(commonLoopDir, \loopScore_chromosight.tsv\)))
loop_hicdcp <- as_tibble(fread(here(commonLoopDir, \loopScore_hicdcp.tsv\)))
loop_Hansen <- as_tibble(fread(here(commonLoopDir, \loopScore_Hansen.tsv\)))
loop_Tjian_chromo <- as_tibble(fread(here(commonLoopDir, \../loop_Tjian/TjianChromo_union_allRes.bedpe\))) %>%
dplyr::filter(abs(V5-V2) <= 2000000)
colnames(loop_Tjian_chromo) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\)
loop_Tjian_mustache <- as_tibble(fread(here(commonLoopDir, \../loop_Tjian/TjianMustache_union_allRes.bedpe\))) %>%
dplyr::filter(abs(V5-V2) <= 2000000)
colnames(loop_Tjian_mustache) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\)
```
<!-- rnb-source-end -->```r
loop_chromo <- as_tibble(fread(here(commonLoopDir, \loopScore_chromosight.tsv\)))
loop_hicdcp <- as_tibble(fread(here(commonLoopDir, \loopScore_hicdcp.tsv\)))
loop_Hansen <- as_tibble(fread(here(commonLoopDir, \loopScore_Hansen.tsv\)))
loop_Tjian_chromo <- as_tibble(fread(here(commonLoopDir, \../loop_Tjian/TjianChromo_union_allRes.bedpe\))) %>%
dplyr::filter(abs(V5-V2) <= 2000000)
colnames(loop_Tjian_chromo) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\)
loop_Tjian_mustache <- as_tibble(fread(here(commonLoopDir, \../loop_Tjian/TjianMustache_union_allRes.bedpe\))) %>%
dplyr::filter(abs(V5-V2) <= 2000000)
colnames(loop_Tjian_mustache) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\)
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
# ANALYSIS
## [1] Comparing loops
### Common stuffs
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
# helper that only uses the two anchors
make_gi <- function(df) {
a1 <- GRanges(df$chrom1, IRanges(df$start1, df$end1))
a2 <- GRanges(df$chrom2, IRanges(df$start2, df$end2))
GInteractions(a1, a2)
}
# Expand each anchor to 10 kb for comparison
expand_anchor <- function(gi) {
a1 <- anchors(gi, \first\)
a2 <- anchors(gi, \second\)
a1r <- resize(a1, width = pmax(width(a1), expandSize), fix = \center\)
a2r <- resize(a2, width = pmax(width(a2), expandSize), fix = \center\)
GInteractions(a1r, a2r)
}
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
# helper that only uses the two anchors
make_gi <- function(df) {
a1 <- GRanges(df$chrom1, IRanges(df$start1, df$end1))
a2 <- GRanges(df$chrom2, IRanges(df$start2, df$end2))
GInteractions(a1, a2)
}
# Expand each anchor to 10 kb for comparison
expand_anchor <- function(gi) {
a1 <- anchors(gi, \first\)
a2 <- anchors(gi, \second\)
a1r <- resize(a1, width = pmax(width(a1), expandSize), fix = \center\)
a2r <- resize(a2, width = pmax(width(a2), expandSize), fix = \center\)
GInteractions(a1r, a2r)
}
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Import loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
for(expandSize in c(5000, 10000, 25000)){
gi1 <- expand_anchor(make_gi(loop_chromo))
gi2 <- expand_anchor(make_gi(loop_hicdcp))
gi3 <- expand_anchor(make_gi(loop_Hansen))
gi_all <- c(gi1, gi2, gi3)
# 4) determine membership via Chebyshev distance = 1 bin(10 kb)
in1 <- overlapsAny(gi_all, gi1, maxgap=expandSize, type=\equal\)
in2 <- overlapsAny(gi_all, gi2, maxgap=expandSize, type=\equal\)
in3 <- overlapsAny(gi_all, gi3, maxgap=expandSize, type=\equal\)
membership_df <- tibble(
loop = seq_along(gi_all),
set1 = in1,
set2 = in2,
set3 = in3
)
membership_df <- membership_df %>%
rename(
Chromosight = set1,
HiCDCP = set2,
Hansen = set3
)
# 5) UpSet plot of all combinations
p <- upset(
membership_df,
intersect = c(\Chromosight\,\HiCDCP\,\Hansen\),
name = \\,
base_annotations = list(
'Loop #' = intersection_size()
)
) + ggtitle(paste0(expandSize, \bp expanded```r
for(expandSize in c(5000, 10000, 25000)){
gi1 <- expand_anchor(make_gi(loop_chromo))
gi2 <- expand_anchor(make_gi(loop_hicdcp))
gi3 <- expand_anchor(make_gi(loop_Hansen))
gi_all <- c(gi1, gi2, gi3)
# 4) determine membership via Chebyshev distance = 1 bin(10 kb)
in1 <- overlapsAny(gi_all, gi1, maxgap=expandSize, type=\equal\)
in2 <- overlapsAny(gi_all, gi2, maxgap=expandSize, type=\equal\)
in3 <- overlapsAny(gi_all, gi3, maxgap=expandSize, type=\equal\)
membership_df <- tibble(
loop = seq_along(gi_all),
set1 = in1,
set2 = in2,
set3 = in3
)
membership_df <- membership_df %>%
rename(
Chromosight = set1,
HiCDCP = set2,
Hansen = set3
)
# 5) UpSet plot of all combinations
p <- upset(
membership_df,
intersect = c(\Chromosight\,\HiCDCP\,\Hansen\),
name = \\,
base_annotations = list(
'Loop #' = intersection_size()
)
) + ggtitle(paste0(expandSize, \bp expanded
<!-- rnb-source-begin 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 -->
```r
for(expandSize in c(5000, 10000, 25000)){
gi1 <- expand_anchor(make_gi(loop_chromo))
gi2 <- expand_anchor(make_gi(loop_Tjian_chromo))
gi3 <- expand_anchor(make_gi(loop_Tjian_mustache))
gi_all <- c(gi1, gi2, gi3)
# 4) determine membership via Chebyshev distance = 1 bin(10 kb)
in1 <- overlapsAny(gi_all, gi1, maxgap=expandSize, type=\equal\)
in2 <- overlapsAny(gi_all, gi2, maxgap=expandSize, type=\equal\)
in3 <- overlapsAny(gi_all, gi3, maxgap=expandSize, type=\equal\)
membership_df <- tibble(
loop = seq_along(gi_all),
set1 = in1,
set2 = in2,
set3 = in3
)
membership_df <- membership_df %>%
rename(
Lee_chromo = set1,
Tjian_chromo = set2,
Tjian_mustache = set3
)
# 5) UpSet plot of all combinations
p <- upset(
membership_df,
intersect = c(\Lee_chromo\,\Tjian_chromo\,\Tjian_mustache\),
name = \\,
base_annotations = list(
'Loop #' = intersection_size()
)
) + ggtitle(paste0(expandSize, \bp expanded```r
for(expandSize in c(5000, 10000, 25000)){
gi1 <- expand_anchor(make_gi(loop_chromo))
gi2 <- expand_anchor(make_gi(loop_Tjian_chromo))
gi3 <- expand_anchor(make_gi(loop_Tjian_mustache))
gi_all <- c(gi1, gi2, gi3)
# 4) determine membership via Chebyshev distance = 1 bin(10 kb)
in1 <- overlapsAny(gi_all, gi1, maxgap=expandSize, type=\equal\)
in2 <- overlapsAny(gi_all, gi2, maxgap=expandSize, type=\equal\)
in3 <- overlapsAny(gi_all, gi3, maxgap=expandSize, type=\equal\)
membership_df <- tibble(
loop = seq_along(gi_all),
set1 = in1,
set2 = in2,
set3 = in3
)
membership_df <- membership_df %>%
rename(
Lee_chromo = set1,
Tjian_chromo = set2,
Tjian_mustache = set3
)
# 5) UpSet plot of all combinations
p <- upset(
membership_df,
intersect = c(\Lee_chromo\,\Tjian_chromo\,\Tjian_mustache\),
name = \\,
base_annotations = list(
'Loop #' = intersection_size()
)
) + ggtitle(paste0(expandSize, \bp expanded
<!-- rnb-source-begin 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 -->
```r
draw_donut <- function(loop, name){
n <- nrow(loop)
temp <- loop %>% dplyr::select(id, Anno) %>%
dplyr::mutate(AnnoGroup = case_when(
Anno %in% c(\P-P\, \P-E\, \E-E\) ~ \reg_reg\,
Anno %in% c(\P-SP\, \P-SE\, \E-SP\, \E-SE\) ~ \reg_strReg\,
Anno %in% c(\P-S\, \E-S\) ~ \reg_str\,
Anno %in% c(\SP-SP\, \SP-SE\, \SE-SE\) ~ \strReg_strReg\,
Anno %in% c(\SP-S\, \SE-S\) ~ \strReg_str\,
Anno %in% c(\P-X\, \E-X\) ~ \reg_other\,
Anno %in% c(\SP-X\, \SE-X\) ~ \strReg_other\,
Anno %in% c(\S-S\, \S-X\) ~ \str\,
Anno %in% c(\X-X\) ~ \other\,
TRUE ~ NA
))
temp$AnnoGroup <- factor(temp$AnnoGroup, levels = c(\reg_reg\,
\reg_strReg\, \reg_str\,
\strReg_strReg\, \strReg_str\,
\reg_other\, \strReg_other\,
\str\, \other\))
pie_df <- temp %>%
as_tibble() %>% # if temp was a list, this will error early
group_by(AnnoGroup) %>%
summarise(n = n(), .groups = \drop\) %>%
mutate(label = paste0(AnnoGroup, \\n(\, n, \)\))
p <- ggplot(pie_df, aes(x = 2, y = n, fill = AnnoGroup)) +
geom_col(color = \black\, width = 1) +
coord_polar(theta = \y\) +
xlim(0.5, 2.5) + # <- creates the “hole” by clipping the center
geom_text(aes(label = label),
position = position_stack(vjust = 0.5),
size = 3) +
theme_void() +
theme(legend.position = \none\,
plot.title = element_text(hjust = 0.5)
) +
ggtitle(paste0(name, \\n\, n, \ loops\))
width = 3
height = 3
svglite(here(figDir,
paste0(\donut_loopAnno_\, name, \.svg\)),
width = width, height = height)
plot(p)
dev.off()
}
draw_donut(loop_chromo, \chromosight\)
```
<!-- rnb-source-end -->```r
draw_donut <- function(loop, name){
n <- nrow(loop)
temp <- loop %>% dplyr::select(id, Anno) %>%
dplyr::mutate(AnnoGroup = case_when(
Anno %in% c(\P-P\, \P-E\, \E-E\) ~ \reg_reg\,
Anno %in% c(\P-SP\, \P-SE\, \E-SP\, \E-SE\) ~ \reg_strReg\,
Anno %in% c(\P-S\, \E-S\) ~ \reg_str\,
Anno %in% c(\SP-SP\, \SP-SE\, \SE-SE\) ~ \strReg_strReg\,
Anno %in% c(\SP-S\, \SE-S\) ~ \strReg_str\,
Anno %in% c(\P-X\, \E-X\) ~ \reg_other\,
Anno %in% c(\SP-X\, \SE-X\) ~ \strReg_other\,
Anno %in% c(\S-S\, \S-X\) ~ \str\,
Anno %in% c(\X-X\) ~ \other\,
TRUE ~ NA
))
temp$AnnoGroup <- factor(temp$AnnoGroup, levels = c(\reg_reg\,
\reg_strReg\, \reg_str\,
\strReg_strReg\, \strReg_str\,
\reg_other\, \strReg_other\,
\str\, \other\))
pie_df <- temp %>%
as_tibble() %>% # if temp was a list, this will error early
group_by(AnnoGroup) %>%
summarise(n = n(), .groups = \drop\) %>%
mutate(label = paste0(AnnoGroup, \\n(\, n, \)\))
p <- ggplot(pie_df, aes(x = 2, y = n, fill = AnnoGroup)) +
geom_col(color = \black\, width = 1) +
coord_polar(theta = \y\) +
xlim(0.5, 2.5) + # <- creates the “hole” by clipping the center
geom_text(aes(label = label),
position = position_stack(vjust = 0.5),
size = 3) +
theme_void() +
theme(legend.position = \none\,
plot.title = element_text(hjust = 0.5)
) +
ggtitle(paste0(name, \\n\, n, \ loops\))
width = 3
height = 3
svglite(here(figDir,
paste0(\donut_loopAnno_\, name, \.svg\)),
width = width, height = height)
plot(p)
dev.off()
}
draw_donut(loop_chromo, \chromosight\)
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcblxuPCEtLSBybmItc291cmNlLWJlZ2luIGV5SmtZWFJoSWpvaVlHQmdjbHh1WkhKaGQxOWtiMjUxZENoc2IyOXdYMmhwWTJSamNDd2dYQ0pvYVdOa1kzQmNJaWxjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmRyYXdfZG9udXQobG9vcF9oaWNkY3AsIFxcaGljZGNwXFwpXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
null device 1
draw_donut(loop_hicdcp, \hicdcp\)
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVpISmhkMTlrYjI1MWRDaHNiMjl3WDJocFkyUmpjQ3dnWEZ4b2FXTmtZM0JjWENsY2JtQmdZRnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbmBgYHJcbmRyYXdfZG9udXQobG9vcF9oaWNkY3AsIFxcaGljZGNwXFwpXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZHJhd19kb251dChsb29wX2hpY2RjcCwgXFxoaWNkY3BcXClcbmBgYFxuYGBgIn0= -->
```r
```r
draw_donut(loop_hicdcp, \hicdcp\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcblxuPCEtLSBybmItc291cmNlLWJlZ2luIGV5SmtZWFJoSWpvaVlHQmdjbHh1WkhKaGQxOWtiMjUxZENoc2IyOXdYMGhoYm5ObGJpd2dYQ0pJWVc1elpXNWNJaWxjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmRyYXdfZG9udXQobG9vcF9IYW5zZW4sIFxcSGFuc2VuXFwpXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
null device 1
draw_donut(loop_Hansen, \Hansen\)
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVpISmhkMTlrYjI1MWRDaHNiMjl3WDBoaGJuTmxiaXdnWEZ4SVlXNXpaVzVjWENsY2JtQmdZRnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbmBgYHJcbmRyYXdfZG9udXQobG9vcF9IYW5zZW4sIFxcSGFuc2VuXFwpXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZHJhd19kb251dChsb29wX0hhbnNlbiwgXFxIYW5zZW5cXClcbmBgYFxuYGBgIn0= -->
```r
```r
draw_donut(loop_Hansen, \Hansen\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Tjian
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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-->\n\n```r\nplot_loop_scores_and_save_bedpe <- function(df, col_x, col_y, \n                                            col_x_ooe, col_y_ooe, pseudo_ooe = 0.01,\n                                            annoList,\n                                            name, title, figDir, loopDir,\n                                            diffCutoff = 0.2, density_n = 200,\n                                            xmin = -0.5, xmax = 1,\n                                            ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  col_x_ooe_q <- enquo(col_x_ooe)\n  col_y_ooe_q <- enquo(col_y_ooe)\n  \n  # Create a single, unified dataframe for plotting and saving as bedpe\n  df_temp <- df %>%\n    dplyr::filter(Anno_simple %in% annoList) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, x = !!col_x_q, y = !!col_y_q,\n                  x_ooe = !!col_x_ooe_q, y_ooe = !!col_y_ooe_q) %>%\n    dplyr::mutate(diff = y-x,\n                  density = get_density(x, y, n = density_n)) %>%\n    dplyr::mutate(updown = case_when(\n      diff > diffCutoff  ~ \\UP\\,\n      diff < -diffCutoff ~ \\DOWN\\,\n      TRUE               ~ \\NO\\))\n  \n  # Export as bedpe\n  bedpeUP <- df_temp %>% dplyr::filter(updown == \\UP\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeUP, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_UP.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  bedpeNO <- df_temp %>% dplyr::filter(updown == \\NO\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeNO, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_NO.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  bedpeDOWN <- df_temp %>% dplyr::filter(updown == \\DOWN\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeDOWN, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_DOWN.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  \n  \n  plot_df <- df_temp %>%\n    dplyr::select(id, x, y, updown, density) %>%\n    arrange(density)\n  \n  plot_df$updown <- factor(plot_df$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  # Calculate a global density range to make scales comparable\n  density_limits <- range(plot_df$density, na.rm = TRUE)\n  \n  # ------ Scatterplot ------ \n  p1 <- ggplot(plot_df, aes(x = x, y = y)) +\n    # Layer 1: Plot \\NO\\ points with a grey scale\n    geom_point(data = . %>% filter(updown == \\NO\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = \\grey85\\, high = \\grey30\\,\n      limits = density_limits, name = \\Density (NO)\\\n    ) +\n    \n    # Introduce a new scale for the \\UP\\ points\n    new_scale_color() +\n    # Layer 2: Plot \\UP\\ points with a red scale\n    geom_point(data = . %>% filter(updown == \\UP\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = weak_red, high = strong_red, # Light to dark red\n      limits = density_limits, name = \\Density (UP)\\\n    ) +\n    \n    # Introduce a new scale for the \\DOWN\\ points\n    new_scale_color() +\n    # Layer 3: Plot \\DOWN\\ points with a blue scale\n    geom_point(data = . %>% filter(updown == \\DOWN\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = weak_blue, high = strong_blue, # Light to dark blue\n      limits = density_limits, name = \\Density (DOWN)\\\n    ) +\n    \n    # Add annotations and theme elements\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    theme_classic() +\n    theme(\n      legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n    labs(x = quo_name(col_x_q), y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch  \n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = width, height = height)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = width, height = height, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # ------ Pie chart ------ \n  pie_df <- plot_df %>%\n    dplyr::count(updown) %>%\n    mutate(\n      pct   = n / sum(n) * 100,\n      label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct, 1), \\%)\\)\n    )\n  \n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(\n      aes(label = label),\n      position = position_stack(vjust = 0.5),\n      size     = fontSizeM / 2\n    ) +\n    theme_void() + theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(\\UP\\ = strong_red, \\NO\\ = no_grey, \\DOWN\\ = strong_blue))\n  \n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n  \n  \n  # ------ Violin plot of ooe ------ \n  plot_df_ooe <- df_temp %>%\n    dplyr::select(id, x_ooe, y_ooe, updown) %>%\n    dplyr::filter(!is.na(x_ooe), !is.na(y_ooe)) %>%\n    dplyr::mutate(\n      x_ooe = if_else(x_ooe == 0, pseudo_ooe, x_ooe),\n      y_ooe = if_else(y_ooe == 0, pseudo_ooe, y_ooe),\n      fc    = log2(y_ooe / x_ooe)\n    )\n  \n  plot_df_ooe$updown <- factor(plot_df_ooe$updown, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  p3 <- ggplot(plot_df_ooe, aes(x = updown, y = fc, fill = updown)) +\n    geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, show.legend = FALSE) + \n    geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n    theme_classic() + \n    stat_summary(\n      aes(group = updown), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    coord_cartesian(ylim = c(-3, 3)) +\n    scale_fill_manual(values = c(strong_red, \\grey30\\, strong_blue)) +\n    theme(\n      axis.title.x = element_blank(),\n      axis.title.y = element_text(\n        size = fontSizeM,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        size = fontSizeM,\n        angle = 45,      # Rotate x-axis labels 45 degrees\n        hjust = 1,       # Adjust horizontal justification\n        vjust = 1        # Adjust vertical justification\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) +labs(y = \\log2 fc of obs/exp\\)  +\n    geom_hline(yintercept = 0,\n               alpha = 1,\n               color = \\black\\,\n               size = lineThick*mmToLineUnit,\n               lineend = \\square\\) +\n    geom_hline(yintercept = c(-0.5, 0.5),\n               alpha = 0.5,\n               color = \\black\\,\n               size = lineThick*mmToLineUnit,\n               lineend = \\square\\, linetype = \\dashed\\)\n   \n  \n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch\n  fileName <- paste0(\\violin_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p3)\n  dev.off()  \n}\n\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_chromosight.tsv\\))\n\n\nplot_loop_scores_and_save_bedpe(data, score_DMSO, score_dTAG, \n                                ooe_DMSO, ooe_dTAG, pseudo_ooe = 0.01,\n                                c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                  \\E-E\\, \\E-S\\, \\E-X\\,\n                                  \\S-S\\, \\S-X\\,\n                                  \\X-X\\),\n                                name = \\chromosight_score_all_dTAGvsDMSO\\,\n                                title = \\chromo_score_all_dTAGvsDMSO\\, figDir, commonLoopDir)\n```\n\n<!-- rnb-source-end -->\n"} -->
plot_loop_scores_and_save_bedpe <- function(df, col_x, col_y,
col_x_ooe, col_y_ooe, pseudo_ooe = 0.01,
annoList,
name, title, figDir, loopDir,
diffCutoff = 0.2, density_n = 200,
xmin = -0.5, xmax = 1,
ymin = -0.5, ymax = 1) {
col_x_q <- enquo(col_x)
col_y_q <- enquo(col_y)
col_x_ooe_q <- enquo(col_x_ooe)
col_y_ooe_q <- enquo(col_y_ooe)
# Create a single, unified dataframe for plotting and saving as bedpe
df_temp <- df %>%
dplyr::filter(Anno_simple %in% annoList) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, x = !!col_x_q, y = !!col_y_q,
x_ooe = !!col_x_ooe_q, y_ooe = !!col_y_ooe_q) %>%
dplyr::mutate(diff = y-x,
density = get_density(x, y, n = density_n)) %>%
dplyr::mutate(updown = case_when(
diff > diffCutoff ~ \UP\,
diff < -diffCutoff ~ \DOWN\,
TRUE ~ \NO\))
# Export as bedpe
bedpeUP <- df_temp %>% dplyr::filter(updown == \UP\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)
fwrite(bedpeUP, here(loopDir, paste0(name, \_diffCutoff\, diffCutoff, \_UP.bedpe\)), sep = \\t\, col.names = FALSE)
bedpeNO <- df_temp %>% dplyr::filter(updown == \NO\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)
fwrite(bedpeNO, here(loopDir, paste0(name, \_diffCutoff\, diffCutoff, \_NO.bedpe\)), sep = \\t\, col.names = FALSE)
bedpeDOWN <- df_temp %>% dplyr::filter(updown == \DOWN\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)
fwrite(bedpeDOWN, here(loopDir, paste0(name, \_diffCutoff\, diffCutoff, \_DOWN.bedpe\)), sep = \\t\, col.names = FALSE)
plot_df <- df_temp %>%
dplyr::select(id, x, y, updown, density) %>%
arrange(density)
plot_df$updown <- factor(plot_df$updown, c(\UP\, \NO\, \DOWN\))
# Calculate a global density range to make scales comparable
density_limits <- range(plot_df$density, na.rm = TRUE)
# ------ Scatterplot ------
p1 <- ggplot(plot_df, aes(x = x, y = y)) +
# Layer 1: Plot \NO\ points with a grey scale
geom_point(data = . %>% filter(updown == \NO\) %>% arrange(density),
aes(color = density), size = 0.1) +
scale_color_gradient(
low = \grey85\, high = \grey30\,
limits = density_limits, name = \Density (NO)\
) +
# Introduce a new scale for the \UP\ points
new_scale_color() +
# Layer 2: Plot \UP\ points with a red scale
geom_point(data = . %>% filter(updown == \UP\) %>% arrange(density),
aes(color = density), size = 0.1) +
scale_color_gradient(
low = weak_red, high = strong_red, # Light to dark red
limits = density_limits, name = \Density (UP)\
) +
# Introduce a new scale for the \DOWN\ points
new_scale_color() +
# Layer 3: Plot \DOWN\ points with a blue scale
geom_point(data = . %>% filter(updown == \DOWN\) %>% arrange(density),
aes(color = density), size = 0.1) +
scale_color_gradient(
low = weak_blue, high = strong_blue, # Light to dark blue
limits = density_limits, name = \Density (DOWN)\
) +
# Add annotations and theme elements
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
theme_classic() +
theme(
legend.position = \none\,
plot.title = element_text(
hjust = 0.5,
size = fontSizeS,
family = fontType
),
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
) +
labs(x = quo_name(col_x_q), y = quo_name(col_y_q)) +
ggtitle(title)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\scatterplot_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)),
width = width, height = height)
print(p1)
dev.off()
png(here(figDir, paste0(fileName, \.png\)),
width = width, height = height, units = \in\, res = 600)
print(p1)
dev.off()
# ------ Pie chart ------
pie_df <- plot_df %>%
dplyr::count(updown) %>%
mutate(
pct = n / sum(n) * 100,
label = paste0(updown, \\n\, n, \\n(\, round(pct, 1), \%)\)
)
p2 <- ggplot(pie_df, aes(x = \\, y = n, fill = updown)) +
geom_col(color = \white\) +
coord_polar(theta = \y\) +
geom_text(
aes(label = label),
position = position_stack(vjust = 0.5),
size = fontSizeM / 2
) +
theme_void() + theme(legend.position = \none\) +
scale_fill_manual(values = c(\UP\ = strong_red, \NO\ = no_grey, \DOWN\ = strong_blue))
fileName <- here(figDir, paste0(\piechart_\, name))
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
svglite(paste0(fileName, \.svg\), width = width, height = height)
print(p2)
dev.off()
# ------ Violin plot of ooe ------
plot_df_ooe <- df_temp %>%
dplyr::select(id, x_ooe, y_ooe, updown) %>%
dplyr::filter(!is.na(x_ooe), !is.na(y_ooe)) %>%
dplyr::mutate(
x_ooe = if_else(x_ooe == 0, pseudo_ooe, x_ooe),
y_ooe = if_else(y_ooe == 0, pseudo_ooe, y_ooe),
fc = log2(y_ooe / x_ooe)
)
plot_df_ooe$updown <- factor(plot_df_ooe$updown, levels = c(\UP\, \NO\, \DOWN\))
p3 <- ggplot(plot_df_ooe, aes(x = updown, y = fc, fill = updown)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = updown), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
coord_cartesian(ylim = c(-3, 3)) +
scale_fill_manual(values = c(strong_red, \grey30\, strong_blue)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2 fc of obs/exp\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = c(-0.5, 0.5),
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\violin_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p3)
dev.off()
}
commonLoopDir <- here(\../data/loop_analysis\)
figDir <- here(\../figure/loop_analysis\)
dir.create(figDir, showWarnings = FALSE, recursive = TRUE)
data <- fread(here(commonLoopDir, \loopScore_chromosight.tsv\))
plot_loop_scores_and_save_bedpe(data, score_DMSO, score_dTAG,
ooe_DMSO, ooe_dTAG, pseudo_ooe = 0.01,
c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_score_all_dTAGvsDMSO\,
title = \chromo_score_all_dTAGvsDMSO\, figDir, commonLoopDir)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nplot_loop_scores_and_save_bedpe <- function(df, col_x, col_y, \n                                            col_x_ooe, col_y_ooe, pseudo_ooe = 0.01,\n                                            annoList,\n                                            name, title, figDir, loopDir,\n                                            diffCutoff = 0.2, density_n = 200,\n                                            xmin = -0.5, xmax = 1,\n                                            ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  col_x_ooe_q <- enquo(col_x_ooe)\n  col_y_ooe_q <- enquo(col_y_ooe)\n  \n  # Create a single, unified dataframe for plotting and saving as bedpe\n  df_temp <- df %>%\n    dplyr::filter(Anno_simple %in% annoList) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, x = !!col_x_q, y = !!col_y_q,\n                  x_ooe = !!col_x_ooe_q, y_ooe = !!col_y_ooe_q) %>%\n    dplyr::mutate(diff = y-x,\n                  density = get_density(x, y, n = density_n)) %>%\n    dplyr::mutate(updown = case_when(\n      diff > diffCutoff  ~ \\UP\\,\n      diff < -diffCutoff ~ \\DOWN\\,\n      TRUE               ~ \\NO\\))\n  \n  # Export as bedpe\n  bedpeUP <- df_temp %>% dplyr::filter(updown == \\UP\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeUP, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_UP.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  bedpeNO <- df_temp %>% dplyr::filter(updown == \\NO\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeNO, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_NO.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  bedpeDOWN <- df_temp %>% dplyr::filter(updown == \\DOWN\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeDOWN, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_DOWN.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  \n  \n  plot_df <- df_temp %>%\n    dplyr::select(id, x, y, updown, density) %>%\n    arrange(density)\n  \n  plot_df$updown <- factor(plot_df$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  # Calculate a global density range to make scales comparable\n  density_limits <- range(plot_df$density, na.rm = TRUE)\n  \n  # ------ Scatterplot ------ \n  p1 <- ggplot(plot_df, aes(x = x, y = y)) +\n    # Layer 1: Plot \\NO\\ points with a grey scale\n    geom_point(data = . %>% filter(updown == \\NO\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = \\grey85\\, high = \\grey30\\,\n      limits = density_limits, name = \\Density (NO)\\\n    ) +\n    \n    # Introduce a new scale for the \\UP\\ points\n    new_scale_color() +\n    # Layer 2: Plot \\UP\\ points with a red scale\n    geom_point(data = . %>% filter(updown == \\UP\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = weak_red, high = strong_red, # Light to dark red\n      limits = density_limits, name = \\Density (UP)\\\n    ) +\n    \n    # Introduce a new scale for the \\DOWN\\ points\n    new_scale_color() +\n    # Layer 3: Plot \\DOWN\\ points with a blue scale\n    geom_point(data = . %>% filter(updown == \\DOWN\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = weak_blue, high = strong_blue, # Light to dark blue\n      limits = density_limits, name = \\Density (DOWN)\\\n    ) +\n    \n    # Add annotations and theme elements\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    theme_classic() +\n    theme(\n      legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n    labs(x = quo_name(col_x_q), y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch  \n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = width, height = height)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = width, height = height, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # ------ Pie chart ------ \n  pie_df <- plot_df %>%\n    dplyr::count(updown) %>%\n    mutate(\n      pct   = n / sum(n) * 100,\n      label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct, 1), \\%)\\)\n    )\n  \n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(\n      aes(label = label),\n      position = position_stack(vjust = 0.5),\n      size     = fontSizeM / 2\n    ) +\n    theme_void() + theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(\\UP\\ = strong_red, \\NO\\ = no_grey, \\DOWN\\ = strong_blue))\n  \n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n  \n  \n  # ------ Violin plot of ooe ------ \n  plot_df_ooe <- df_temp %>%\n    dplyr::select(id, x_ooe, y_ooe, updown) %>%\n    dplyr::filter(!is.na(x_ooe), !is.na(y_ooe)) %>%\n    dplyr::mutate(\n      x_ooe = if_else(x_ooe == 0, pseudo_ooe, x_ooe),\n      y_ooe = if_else(y_ooe == 0, pseudo_ooe, y_ooe),\n      fc    = log2(y_ooe / x_ooe)\n    )\n  \n  plot_df_ooe$updown <- factor(plot_df_ooe$updown, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  p3 <- ggplot(plot_df_ooe, aes(x = updown, y = fc, fill = updown)) +\n    geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, show.legend = FALSE) + \n    geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n    theme_classic() + \n    stat_summary(\n      aes(group = updown), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    coord_cartesian(ylim = c(-3, 3)) +\n    scale_fill_manual(values = c(strong_red, \\grey30\\, strong_blue)) +\n    theme(\n      axis.title.x = element_blank(),\n      axis.title.y = element_text(\n        size = fontSizeM,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        size = fontSizeM,\n        angle = 45,      # Rotate x-axis labels 45 degrees\n        hjust = 1,       # Adjust horizontal justification\n        vjust = 1        # Adjust vertical justification\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) +labs(y = \\log2 fc of obs/exp\\)  +\n    geom_hline(yintercept = 0,\n               alpha = 1,\n               color = \\black\\,\n               size = lineThick*mmToLineUnit,\n               lineend = \\square\\) +\n    geom_hline(yintercept = c(-0.5, 0.5),\n               alpha = 0.5,\n               color = \\black\\,\n               size = lineThick*mmToLineUnit,\n               lineend = \\square\\, linetype = \\dashed\\)\n   \n  \n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch\n  fileName <- paste0(\\violin_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p3)\n  dev.off()  \n}\n\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_chromosight.tsv\\))\n\n\nplot_loop_scores_and_save_bedpe(data, score_DMSO, score_dTAG, \n                                ooe_DMSO, ooe_dTAG, pseudo_ooe = 0.01,\n                                c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                  \\E-E\\, \\E-S\\, \\E-X\\,\n                                  \\S-S\\, \\S-X\\,\n                                  \\X-X\\),\n                                name = \\chromosight_score_all_dTAGvsDMSO\\,\n                                title = \\chromo_score_all_dTAGvsDMSO\\, figDir, commonLoopDir)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nplot_loop_scores_and_save_bedpe <- function(df, col_x, col_y, \n                                            col_x_ooe, col_y_ooe, pseudo_ooe = 0.01,\n                                            annoList,\n                                            name, title, figDir, loopDir,\n                                            diffCutoff = 0.2, density_n = 200,\n                                            xmin = -0.5, xmax = 1,\n                                            ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  col_x_ooe_q <- enquo(col_x_ooe)\n  col_y_ooe_q <- enquo(col_y_ooe)\n  \n  # Create a single, unified dataframe for plotting and saving as bedpe\n  df_temp <- df %>%\n    dplyr::filter(Anno_simple %in% annoList) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, x = !!col_x_q, y = !!col_y_q,\n                  x_ooe = !!col_x_ooe_q, y_ooe = !!col_y_ooe_q) %>%\n    dplyr::mutate(diff = y-x,\n                  density = get_density(x, y, n = density_n)) %>%\n    dplyr::mutate(updown = case_when(\n      diff > diffCutoff  ~ \\UP\\,\n      diff < -diffCutoff ~ \\DOWN\\,\n      TRUE               ~ \\NO\\))\n  \n  # Export as bedpe\n  bedpeUP <- df_temp %>% dplyr::filter(updown == \\UP\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeUP, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_UP.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  bedpeNO <- df_temp %>% dplyr::filter(updown == \\NO\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeNO, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_NO.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  bedpeDOWN <- df_temp %>% dplyr::filter(updown == \\DOWN\\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)\n  fwrite(bedpeDOWN, here(loopDir, paste0(name, \\_diffCutoff\\, diffCutoff, \\_DOWN.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n  \n  \n  plot_df <- df_temp %>%\n    dplyr::select(id, x, y, updown, density) %>%\n    arrange(density)\n  \n  plot_df$updown <- factor(plot_df$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  # Calculate a global density range to make scales comparable\n  density_limits <- range(plot_df$density, na.rm = TRUE)\n  \n  # ------ Scatterplot ------ \n  p1 <- ggplot(plot_df, aes(x = x, y = y)) +\n    # Layer 1: Plot \\NO\\ points with a grey scale\n    geom_point(data = . %>% filter(updown == \\NO\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = \\grey85\\, high = \\grey30\\,\n      limits = density_limits, name = \\Density (NO)\\\n    ) +\n    \n    # Introduce a new scale for the \\UP\\ points\n    new_scale_color() +\n    # Layer 2: Plot \\UP\\ points with a red scale\n    geom_point(data = . %>% filter(updown == \\UP\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = weak_red, high = strong_red, # Light to dark red\n      limits = density_limits, name = \\Density (UP)\\\n    ) +\n    \n    # Introduce a new scale for the \\DOWN\\ points\n    new_scale_color() +\n    # Layer 3: Plot \\DOWN\\ points with a blue scale\n    geom_point(data = . %>% filter(updown == \\DOWN\\) %>% arrange(density),\n               aes(color = density), size = 0.1) +\n    scale_color_gradient(\n      low = weak_blue, high = strong_blue, # Light to dark blue\n      limits = density_limits, name = \\Density (DOWN)\\\n    ) +\n    \n    # Add annotations and theme elements\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    theme_classic() +\n    theme(\n      legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n    labs(x = quo_name(col_x_q), y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch  \n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = width, height = height)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = width, height = height, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # ------ Pie chart ------ \n  pie_df <- plot_df %>%\n    dplyr::count(updown) %>%\n    mutate(\n      pct   = n / sum(n) * 100,\n      label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct, 1), \\%)\\)\n    )\n  \n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(\n      aes(label = label),\n      position = position_stack(vjust = 0.5),\n      size     = fontSizeM / 2\n    ) +\n    theme_void() + theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(\\UP\\ = strong_red, \\NO\\ = no_grey, \\DOWN\\ = strong_blue))\n  \n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n  \n  \n  # ------ Violin plot of ooe ------ \n  plot_df_ooe <- df_temp %>%\n    dplyr::select(id, x_ooe, y_ooe, updown) %>%\n    dplyr::filter(!is.na(x_ooe), !is.na(y_ooe)) %>%\n    dplyr::mutate(\n      x_ooe = if_else(x_ooe == 0, pseudo_ooe, x_ooe),\n      y_ooe = if_else(y_ooe == 0, pseudo_ooe, y_ooe),\n      fc    = log2(y_ooe / x_ooe)\n    )\n  \n  plot_df_ooe$updown <- factor(plot_df_ooe$updown, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  p3 <- ggplot(plot_df_ooe, aes(x = updown, y = fc, fill = updown)) +\n    geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, show.legend = FALSE) + \n    geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n    theme_classic() + \n    stat_summary(\n      aes(group = updown), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    coord_cartesian(ylim = c(-3, 3)) +\n    scale_fill_manual(values = c(strong_red, \\grey30\\, strong_blue)) +\n    theme(\n      axis.title.x = element_blank(),\n      axis.title.y = element_text(\n        size = fontSizeM,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        size = fontSizeM,\n        angle = 45,      # Rotate x-axis labels 45 degrees\n        hjust = 1,       # Adjust horizontal justification\n        vjust = 1        # Adjust vertical justification\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) +labs(y = \\log2 fc of obs/exp\\)  +\n    geom_hline(yintercept = 0,\n               alpha = 1,\n               color = \\black\\,\n               size = lineThick*mmToLineUnit,\n               lineend = \\square\\) +\n    geom_hline(yintercept = c(-0.5, 0.5),\n               alpha = 0.5,\n               color = \\black\\,\n               size = lineThick*mmToLineUnit,\n               lineend = \\square\\, linetype = \\dashed\\)\n   \n  \n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch\n  fileName <- paste0(\\violin_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p3)\n  dev.off()  \n}\n\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_chromosight.tsv\\))\n\n\nplot_loop_scores_and_save_bedpe(data, score_DMSO, score_dTAG, \n                                ooe_DMSO, ooe_dTAG, pseudo_ooe = 0.01,\n                                c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                  \\E-E\\, \\E-S\\, \\E-X\\,\n                                  \\S-S\\, \\S-X\\,\n                                  \\X-X\\),\n                                name = \\chromosight_score_all_dTAGvsDMSO\\,\n                                title = \\chromo_score_all_dTAGvsDMSO\\, figDir, commonLoopDir)\n```\n```"} -->
```r
```r
plot_loop_scores_and_save_bedpe <- function(df, col_x, col_y,
col_x_ooe, col_y_ooe, pseudo_ooe = 0.01,
annoList,
name, title, figDir, loopDir,
diffCutoff = 0.2, density_n = 200,
xmin = -0.5, xmax = 1,
ymin = -0.5, ymax = 1) {
col_x_q <- enquo(col_x)
col_y_q <- enquo(col_y)
col_x_ooe_q <- enquo(col_x_ooe)
col_y_ooe_q <- enquo(col_y_ooe)
# Create a single, unified dataframe for plotting and saving as bedpe
df_temp <- df %>%
dplyr::filter(Anno_simple %in% annoList) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, x = !!col_x_q, y = !!col_y_q,
x_ooe = !!col_x_ooe_q, y_ooe = !!col_y_ooe_q) %>%
dplyr::mutate(diff = y-x,
density = get_density(x, y, n = density_n)) %>%
dplyr::mutate(updown = case_when(
diff > diffCutoff ~ \UP\,
diff < -diffCutoff ~ \DOWN\,
TRUE ~ \NO\))
# Export as bedpe
bedpeUP <- df_temp %>% dplyr::filter(updown == \UP\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)
fwrite(bedpeUP, here(loopDir, paste0(name, \_diffCutoff\, diffCutoff, \_UP.bedpe\)), sep = \\t\, col.names = FALSE)
bedpeNO <- df_temp %>% dplyr::filter(updown == \NO\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)
fwrite(bedpeNO, here(loopDir, paste0(name, \_diffCutoff\, diffCutoff, \_NO.bedpe\)), sep = \\t\, col.names = FALSE)
bedpeDOWN <- df_temp %>% dplyr::filter(updown == \DOWN\) %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2)
fwrite(bedpeDOWN, here(loopDir, paste0(name, \_diffCutoff\, diffCutoff, \_DOWN.bedpe\)), sep = \\t\, col.names = FALSE)
plot_df <- df_temp %>%
dplyr::select(id, x, y, updown, density) %>%
arrange(density)
plot_df$updown <- factor(plot_df$updown, c(\UP\, \NO\, \DOWN\))
# Calculate a global density range to make scales comparable
density_limits <- range(plot_df$density, na.rm = TRUE)
# ------ Scatterplot ------
p1 <- ggplot(plot_df, aes(x = x, y = y)) +
# Layer 1: Plot \NO\ points with a grey scale
geom_point(data = . %>% filter(updown == \NO\) %>% arrange(density),
aes(color = density), size = 0.1) +
scale_color_gradient(
low = \grey85\, high = \grey30\,
limits = density_limits, name = \Density (NO)\
) +
# Introduce a new scale for the \UP\ points
new_scale_color() +
# Layer 2: Plot \UP\ points with a red scale
geom_point(data = . %>% filter(updown == \UP\) %>% arrange(density),
aes(color = density), size = 0.1) +
scale_color_gradient(
low = weak_red, high = strong_red, # Light to dark red
limits = density_limits, name = \Density (UP)\
) +
# Introduce a new scale for the \DOWN\ points
new_scale_color() +
# Layer 3: Plot \DOWN\ points with a blue scale
geom_point(data = . %>% filter(updown == \DOWN\) %>% arrange(density),
aes(color = density), size = 0.1) +
scale_color_gradient(
low = weak_blue, high = strong_blue, # Light to dark blue
limits = density_limits, name = \Density (DOWN)\
) +
# Add annotations and theme elements
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
theme_classic() +
theme(
legend.position = \none\,
plot.title = element_text(
hjust = 0.5,
size = fontSizeS,
family = fontType
),
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
) +
labs(x = quo_name(col_x_q), y = quo_name(col_y_q)) +
ggtitle(title)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\scatterplot_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)),
width = width, height = height)
print(p1)
dev.off()
png(here(figDir, paste0(fileName, \.png\)),
width = width, height = height, units = \in\, res = 600)
print(p1)
dev.off()
# ------ Pie chart ------
pie_df <- plot_df %>%
dplyr::count(updown) %>%
mutate(
pct = n / sum(n) * 100,
label = paste0(updown, \\n\, n, \\n(\, round(pct, 1), \%)\)
)
p2 <- ggplot(pie_df, aes(x = \\, y = n, fill = updown)) +
geom_col(color = \white\) +
coord_polar(theta = \y\) +
geom_text(
aes(label = label),
position = position_stack(vjust = 0.5),
size = fontSizeM / 2
) +
theme_void() + theme(legend.position = \none\) +
scale_fill_manual(values = c(\UP\ = strong_red, \NO\ = no_grey, \DOWN\ = strong_blue))
fileName <- here(figDir, paste0(\piechart_\, name))
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
svglite(paste0(fileName, \.svg\), width = width, height = height)
print(p2)
dev.off()
# ------ Violin plot of ooe ------
plot_df_ooe <- df_temp %>%
dplyr::select(id, x_ooe, y_ooe, updown) %>%
dplyr::filter(!is.na(x_ooe), !is.na(y_ooe)) %>%
dplyr::mutate(
x_ooe = if_else(x_ooe == 0, pseudo_ooe, x_ooe),
y_ooe = if_else(y_ooe == 0, pseudo_ooe, y_ooe),
fc = log2(y_ooe / x_ooe)
)
plot_df_ooe$updown <- factor(plot_df_ooe$updown, levels = c(\UP\, \NO\, \DOWN\))
p3 <- ggplot(plot_df_ooe, aes(x = updown, y = fc, fill = updown)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = updown), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
coord_cartesian(ylim = c(-3, 3)) +
scale_fill_manual(values = c(strong_red, \grey30\, strong_blue)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2 fc of obs/exp\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = c(-0.5, 0.5),
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\violin_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p3)
dev.off()
}
commonLoopDir <- here(\../data/loop_analysis\)
figDir <- here(\../figure/loop_analysis\)
dir.create(figDir, showWarnings = FALSE, recursive = TRUE)
data <- fread(here(commonLoopDir, \loopScore_chromosight.tsv\))
plot_loop_scores_and_save_bedpe(data, score_DMSO, score_dTAG,
ooe_DMSO, ooe_dTAG, pseudo_ooe = 0.01,
c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_score_all_dTAGvsDMSO\,
title = \chromo_score_all_dTAGvsDMSO\, figDir, commonLoopDir)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiV2FybmluZyA6RHVwbGljYXRlZCBhZXN0aGV0aWNzIGFmdGVyIG5hbWUgc3RhbmRhcmRpc2F0aW9uOiBjb2xvdXJcbldhcm5pbmcgOkR1cGxpY2F0ZWQgYWVzdGhldGljcyBhZnRlciBuYW1lIHN0YW5kYXJkaXNhdGlvbjogY29sb3VyXG5XYXJuaW5nIDpEdXBsaWNhdGVkIGFlc3RoZXRpY3MgYWZ0ZXIgbmFtZSBzdGFuZGFyZGlzYXRpb246IGNvbG91clxuV2FybmluZyA6RHVwbGljYXRlZCBhZXN0aGV0aWNzIGFmdGVyIG5hbWUgc3RhbmRhcmRpc2F0aW9uOiBjb2xvdXJcbldhcm5pbmcgOkR1cGxpY2F0ZWQgYWVzdGhldGljcyBhZnRlciBuYW1lIHN0YW5kYXJkaXNhdGlvbjogY29sb3VyXG5udWxsIGRldmljZSBcbiAgICAgICAgICAxIFxuIn0= -->
```
Warning :Duplicated aesthetics after name standardisation: colour
Warning :Duplicated aesthetics after name standardisation: colour
Warning :Duplicated aesthetics after name standardisation: colour
Warning :Duplicated aesthetics after name standardisation: colour
Warning :Duplicated aesthetics after name standardisation: colour
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2] Visualizing loop annotation
### [FIG] Loop Anno pie chart
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
<!-- rnb-source-begin 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 -->
```r
draw_donut <- function(loop, name){
n <- nrow(loop)
temp <- loop %>% dplyr::select(id, Anno) %>%
dplyr::mutate(AnnoGroup = case_when(
Anno %in% c("P-P", "P-E", "E-E") ~ "reg_reg",
Anno %in% c("P-SP", "P-SE", "E-SP", "E-SE") ~ "reg_strReg",
Anno %in% c("P-S", "E-S") ~ "reg_str",
Anno %in% c("SP-SP", "SP-SE", "SE-SE") ~ "strReg_strReg",
Anno %in% c("SP-S", "SE-S") ~ "strReg_str",
Anno %in% c("P-X", "E-X") ~ "reg_other",
Anno %in% c("SP-X", "SE-X") ~ "strReg_other",
Anno %in% c("S-S", "S-X") ~ "str",
Anno %in% c("X-X") ~ "other",
TRUE ~ NA
))
temp$AnnoGroup <- factor(temp$AnnoGroup, levels = c("reg_reg",
"reg_strReg", "reg_str",
"strReg_strReg", "strReg_str",
"reg_other", "strReg_other",
"str", "other"))
pie_df <- temp %>%
as_tibble() %>% # if temp was a list, this will error early
group_by(AnnoGroup) %>%
summarise(n = n(), .groups = "drop") %>%
mutate(label = paste0(AnnoGroup, "\n(", n, ")"))
p <- ggplot(pie_df, aes(x = 2, y = n, fill = AnnoGroup)) +
geom_col(color = "black", width = 1) +
coord_polar(theta = "y") +
xlim(0.5, 2.5) + # <- creates the “hole” by clipping the center
geom_text(aes(label = label),
position = position_stack(vjust = 0.5),
size = 3) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5)
) +
ggtitle(paste0(name, "\n", n, " loops"))
width = 3
height = 3
svglite(here(figDir,
paste0("donut_loopAnno_", name, ".svg")),
width = width, height = height)
plot(p)
dev.off()
}
draw_donut(loop_chromo, "chromosight")
draw_donut(loop_hicdcp, "hicdcp")
draw_donut(loop_Hansen, "Hansen")
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [3] Loop analysis
### Chromosight
#### [FIG] Scatterplot
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
plot_loop_scores_and_save_bedpe(data, score_DMSO, score_dTAG,
ooe_DMSO, ooe_dTAG, pseudo_ooe = 0.01,
c("P-P", "P-E", "E-E"),
name = "chromosight_score_complex_reg_dTAGvsDMSO",
title = "chromo_score_complex_reg_dTAGvsDMSO", figDir, commonLoopDir,
anno_col = "Anno")
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdF9sb29wX3Njb3Jlc19hbmRfc2F2ZV9iZWRwZShkYXRhLCBzY29yZV9ETVNPLCBzY29yZV9kVEFHLCBcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgb29lX0RNU08sIG9vZV9kVEFHLCBwc2V1ZG9fb29lID0gMC4wMSxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgYyhcIlMtU1wiLCBcIlMtWFwiKSxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgbmFtZSA9IFwiY2hyb21vc2lnaHRfc2NvcmVfY29tcGxleF9zdHJfZFRBR3ZzRE1TT1wiLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICB0aXRsZSA9IFwiY2hyb21vX3Njb3JlX2NvbXBsZXhfc3RyX2RUQUd2c0RNU09cIiwgZmlnRGlyLCBjb21tb25Mb29wRGlyLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBhbm5vX2NvbCA9IFwiQW5ub1wiKVxuYGBgIn0= -->
```r
plot_loop_scores_and_save_bedpe(data, score_DMSO, score_dTAG,
ooe_DMSO, ooe_dTAG, pseudo_ooe = 0.01,
c("S-S", "S-X"),
name = "chromosight_score_complex_str_dTAGvsDMSO",
title = "chromo_score_complex_str_dTAGvsDMSO", figDir, commonLoopDir,
anno_col = "Anno")
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdF9sb29wX3Njb3Jlc19hbmRfc2F2ZV9iZWRwZShkYXRhLCBzY29yZV9ETVNPLCBzY29yZV9kVEFHLCBcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgb29lX0RNU08sIG9vZV9kVEFHLCBwc2V1ZG9fb29lID0gMC4wMSxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgYyhcIlMtU1wiKSxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgbmFtZSA9IFwiY2hyb21vc2lnaHRfc2NvcmVfY29tcGxleF9zdHIyX2RUQUd2c0RNU09cIixcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgdGl0bGUgPSBcImNocm9tb19zY29yZV9jb21wbGV4X3N0cjJfX2RUQUd2c0RNU09cIiwgZmlnRGlyLCBjb21tb25Mb29wRGlyLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBhbm5vX2NvbCA9IFwiQW5ub1wiKVxuYGBgIn0= -->
```r
plot_loop_scores_and_save_bedpe(data, score_DMSO, score_dTAG,
ooe_DMSO, ooe_dTAG, pseudo_ooe = 0.01,
c("S-S"),
name = "chromosight_score_complex_str2_dTAGvsDMSO",
title = "chromo_score_complex_str2__dTAGvsDMSO", figDir, commonLoopDir,
anno_col = "Anno")
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### [FIG] LOLA on anchor
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
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```r
#-------------------------------------------------------------------------------
# LOADING LOLA DB
library("simpleCache")
library("LOLA")
lolaDB = loadRegionDB("/Volumes/UKJIN_SSD/MtoG1_analysis_code/reference/LOLACore_cached/mm10")
# FUNCTIONS
extractAnchor <- function(loop){
anchor1 <- loop %>% dplyr::select(c(1, 2, 3))
colnames(anchor1) <- c("chr", "start", "end")
anchor2 <- loop %>% dplyr::select(c(4, 5, 6))
colnames(anchor2) <- c("chr", "start", "end")
anchors <- reduce(makeGRangesFromDataFrame(bind_rows(anchor1, anchor2)))
return(anchors)
}
atac <- fread(here(refDir, "mm10", "GSM3106257_ATAC_ESC_1.bed")) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c("chr", "start", "end")
atac.gr <- makeGRangesFromDataFrame(atac)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### All loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
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```r
temp <- temp %>% dplyr::filter(target %in% targetList)
temp$target <- factor(temp$target, levels = rev(targetList))
qValueLogMax <- 50
temp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))
p <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = "white", high = "#CB333A",
limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = "#000000"
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = "#000000"
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = "#000000",
size = lineThick*mmToLineUnit,
lineend = "square"
),
axis.ticks = element_line(
color = "#000000",
size = lineThick*mmToLineUnit,
lineend = "square"
),
panel.background = element_rect(fill = "transparent"),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
figDir <- here("../figure/loop_analysis")
fileName <- paste0("LOLA_", name)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.9)*mmToInch
png(here(figDir, paste0(fileName, ".png")), res = 600, unit = "in", height = height, width = width)
print(p)
dev.off()
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3ZnbGl0ZShoZXJlKGZpZ0RpciwgcGFzdGUwKGZpbGVOYW1lLCBcIi5zdmdcIikpLCAgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmBgYCJ9 -->
```r
svglite(here(figDir, paste0(fileName, ".svg")), height = height, width = width)
print(p)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVaR1YyTG05bVppZ3BYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmRldi5vZmYoKVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGV2Lm9mZigpXG5gYGAifQ== -->
```r
dev.off()
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### Reg loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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ZmFtaWx5ID0gZm9udFR5cGUsIHNpemUgPSBmb250U2l6ZVMpXG4gIClcblxuZmlnRGlyIDwtIGhlcmUoXCIuLi9maWd1cmUvbG9vcF9hbmFseXNpc1wiKVxuXG5maWxlTmFtZSA8LSBwYXN0ZTAoXCJMT0xBX1wiLCBuYW1lKVxud2lkdGggPC0gcGFuZWxTaXplKDEuNSkqbW1Ub0luY2hcbmhlaWdodCA8LSBwYW5lbFNpemUoMS45KSptbVRvSW5jaFxucG5nKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFwiLnBuZ1wiKSksIHJlcyA9IDYwMCwgdW5pdCA9IFwiaW5cIiwgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuYGBgIn0= -->\n\n```r\n#-------------------------------------------------------------------------------\n# LOADING LOOPS\n### Importing differential regulatory loops & extract anchor\ncommonLoopDir <- here(\"../data/loop_analysis\")\nloop.all <- fread(here(commonLoopDir, \"loopScore_chromosight.tsv\"))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.up <- fread(here(commonLoopDir, \"chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_UP.bedpe\"))\nanchor.up <- (extractAnchor(loop.up))\noverlaps <- findOverlaps(anchor.up, atac.gr)\nanchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.no <- fread(here(commonLoopDir, \"chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_NO.bedpe\"))\nanchor.no <- (extractAnchor(loop.no))\noverlaps <- findOverlaps(anchor.no, atac.gr)\nanchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.upno <- bind_rows(loop.up, loop.no)\nanchor.upno <- (extractAnchor(loop.upno))\noverlaps <- findOverlaps(anchor.upno, atac.gr)\nanchor.upno <- pintersect(anchor.upno[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.down <- fread(here(commonLoopDir, \"chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_DOWN.bedpe\"))\nanchor.down <- (extractAnchor(loop.down))\noverlaps <- findOverlaps(anchor.down, atac.gr)\nanchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n#-------------------------------------------------------------------------------\n# RUNNING LOLA\n# UP\nlolaDir <- here(commonLoopDir, \"LOLA\")\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n\nname <- \"chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2\"\nresult <- runLOLA(anchor.up, anchor.all, lolaDB)\ntb <- as_tibble(result)\nfwrite(tb, here(lolaDir, paste0(\"LOLA_\", name, \"UP_ATAC.tsv\")), sep = \"\\t\")\n\n# NO\nresult <- runLOLA(anchor.no, anchor.all, lolaDB)\ntb <- as_tibble(result)\nfwrite(tb, here(lolaDir, paste0(\"LOLA_\", name, \"NO_ATAC.tsv\")), sep = \"\\t\")\n\n# UPNO\nresult <- runLOLA(anchor.upno, anchor.all, lolaDB)\ntb <- as_tibble(result)\nfwrite(tb, here(lolaDir, paste0(\"LOLA_\", name, \"UPNO_ATAC.tsv\")), sep = \"\\t\")\n\n# DOWN\nresult <- runLOLA(anchor.down, anchor.all, lolaDB)\ntb <- as_tibble(result)\nfwrite(tb, here(lolaDir, paste0(\"LOLA_\", name, \"DOWN_ATAC.tsv\")), sep = \"\\t\")\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, paste0(\"LOLA_\", name, \"UP_ATAC.tsv\"))) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \"UP\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, paste0(\"LOLA_\", name, \"NO_ATAC.tsv\"))) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \"NO\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.upno <- fread(here(lolaDir, paste0(\"LOLA_\", name, \"UPNO_ATAC.tsv\"))) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \"UP/NO\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\nfwrite(tb.upno %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, paste0(\"LOLA_\", name, \"UPNO_ATAC_pub.tsv\")), sep = \"\\t\")\n\ntb.down <- fread(here(lolaDir, paste0(\"LOLA_\", name, \"DOWN_ATAC.tsv\"))) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \"DOWN\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.down %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, paste0(\"LOLA_\", name, \"DOWN_ATAC__pub.tsv\")), sep = \"\\t\")\n\n\ntemp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.upno, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \"UP/NO\") %>% arrange(desc(oddsRatio)))$target,\n           (temp %>% dplyr::filter(group == \"DOWN\") %>% arrange(desc(oddsRatio)))$target)\norder <- unique(order)\n\ntemp$target <- factor(temp$target, levels = rev(order))\ntemp$group <- factor(temp$group, levels = c(\"UP/NO\", \"DOWN\"))\n\ntargetList <- c(\"POLR2A\", \"CTR9\", \"ARR4\", \"ELL2\",\n                \"MED1\", \"MED12\", \"TBP\", \"TAF1\", \"TAF2\",\n                \"ESRRB\", \"KLF4\", \"NANOG\", \"POU5F1\", \"SOX2\", \"MYCN\", \"GASTA4\", \"STAT3\", \"E2F1\", \"YY1\",\n                \"P300\", \"DPY30\", \"RBBP5\", \"KDM2B\", \"KDM4B\", \"KDM6B\", \"TET1\",\n                \"CBX7\", \"RING1B\", \"RNF2\", \"EZH2\", \"SUZ12\", \"JARID2\", \"MTF2\", \"PHF19\")\n\ntemp <- temp %>% dplyr::filter(target %in% targetList)\ntemp$target <- factor(temp$target, levels = rev(targetList))\n\n\nqValueLogMax <- 50\ntemp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))\n\np <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \"white\", high = \"#CB333A\",\n                      limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfigDir <- here(\"../figure/loop_analysis\")\n\nfileName <- paste0(\"LOLA_\", name)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(1.9)*mmToInch\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n#-------------------------------------------------------------------------------\n# LOADING LOOPS\n### Importing differential regulatory loops & extract anchor\ncommonLoopDir <- here(\"../data/loop_analysis\")\nloop.all <- fread(here(commonLoopDir, \"loopScore_chromosight.tsv\"))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.up <- fread(here(commonLoopDir, \"chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_UP.bedpe\"))\nanchor.up <- (extractAnchor(loop.up))\noverlaps <- findOverlaps(anchor.up, atac.gr)\nanchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.no <- fread(here(commonLoopDir, \"chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_NO.bedpe\"))\nanchor.no <- (extractAnchor(loop.no))\noverlaps <- findOverlaps(anchor.no, atac.gr)\nanchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.upno <- bind_rows(loop.up, loop.no)\nanchor.upno <- (extractAnchor(loop.upno))\noverlaps <- findOverlaps(anchor.upno, atac.gr)\nanchor.upno <- pintersect(anchor.upno[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.down <- fread(here(commonLoopDir, \"chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_DOWN.bedpe\"))\nanchor.down <- (extractAnchor(loop.down))\noverlaps <- findOverlaps(anchor.down, atac.gr)\nanchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n#-------------------------------------------------------------------------------\n# RUNNING LOLA\n# UP\nlolaDir <- here(commonLoopDir, \"LOLA\")\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n\nname <- \"chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2\"\nresult <- runLOLA(anchor.up, anchor.all, lolaDB)\ntb <- as_tibble(result)\nfwrite(tb, here(lolaDir, paste0(\"LOLA_\", name, \"UP_ATAC.tsv\")), sep = \"\\t\")\n\n# NO\nresult <- runLOLA(anchor.no, anchor.all, lolaDB)\ntb <- as_tibble(result)\nfwrite(tb, here(lolaDir, paste0(\"LOLA_\", name, \"NO_ATAC.tsv\")), sep = \"\\t\")\n\n# UPNO\nresult <- runLOLA(anchor.upno, anchor.all, lolaDB)\ntb <- as_tibble(result)\nfwrite(tb, here(lolaDir, paste0(\"LOLA_\", name, \"UPNO_ATAC.tsv\")), sep = \"\\t\")\n\n# DOWN\nresult <- runLOLA(anchor.down, anchor.all, lolaDB)\ntb <- as_tibble(result)\nfwrite(tb, here(lolaDir, paste0(\"LOLA_\", name, \"DOWN_ATAC.tsv\")), sep = \"\\t\")\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, paste0(\"LOLA_\", name, \"UP_ATAC.tsv\"))) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \"UP\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, paste0(\"LOLA_\", name, \"NO_ATAC.tsv\"))) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \"NO\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.upno <- fread(here(lolaDir, paste0(\"LOLA_\", name, \"UPNO_ATAC.tsv\"))) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \"UP/NO\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\nfwrite(tb.upno %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, paste0(\"LOLA_\", name, \"UPNO_ATAC_pub.tsv\")), sep = \"\\t\")\n\ntb.down <- fread(here(lolaDir, paste0(\"LOLA_\", name, \"DOWN_ATAC.tsv\"))) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \"DOWN\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.down %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, paste0(\"LOLA_\", name, \"DOWN_ATAC__pub.tsv\")), sep = \"\\t\")\n\n\ntemp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.upno, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \"UP/NO\") %>% arrange(desc(oddsRatio)))$target,\n           (temp %>% dplyr::filter(group == \"DOWN\") %>% arrange(desc(oddsRatio)))$target)\norder <- unique(order)\n\ntemp$target <- factor(temp$target, levels = rev(order))\ntemp$group <- factor(temp$group, levels = c(\"UP/NO\", \"DOWN\"))\n\ntargetList <- c(\"POLR2A\", \"CTR9\", \"ARR4\", \"ELL2\",\n                \"MED1\", \"MED12\", \"TBP\", \"TAF1\", \"TAF2\",\n                \"ESRRB\", \"KLF4\", \"NANOG\", \"POU5F1\", \"SOX2\", \"MYCN\", \"GASTA4\", \"STAT3\", \"E2F1\", \"YY1\",\n                \"P300\", \"DPY30\", \"RBBP5\", \"KDM2B\", \"KDM4B\", \"KDM6B\", \"TET1\",\n                \"CBX7\", \"RING1B\", \"RNF2\", \"EZH2\", \"SUZ12\", \"JARID2\", \"MTF2\", \"PHF19\")\n\ntemp <- temp %>% dplyr::filter(target %in% targetList)\ntemp$target <- factor(temp$target, levels = rev(targetList))\n\n\nqValueLogMax <- 50\ntemp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))\n\np <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \"white\", high = \"#CB333A\",\n                      limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfigDir <- here(\"../figure/loop_analysis\")\n\nfileName <- paste0(\"LOLA_\", name)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(1.9)*mmToInch\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\n```"} -->
```r
#-------------------------------------------------------------------------------
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
commonLoopDir <- here("../data/loop_analysis")
loop.all <- fread(here(commonLoopDir, "loopScore_chromosight.tsv"))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(commonLoopDir, "chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_UP.bedpe"))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(commonLoopDir, "chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_NO.bedpe"))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.upno <- bind_rows(loop.up, loop.no)
anchor.upno <- (extractAnchor(loop.upno))
overlaps <- findOverlaps(anchor.upno, atac.gr)
anchor.upno <- pintersect(anchor.upno[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(commonLoopDir, "chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2_DOWN.bedpe"))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
#-------------------------------------------------------------------------------
# RUNNING LOLA
# UP
lolaDir <- here(commonLoopDir, "LOLA")
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
name <- "chromosight_score_complex_reg_dTAGvsDMSO_diffCutoff0.2"
result <- runLOLA(anchor.up, anchor.all, lolaDB)
tb <- as_tibble(result)
fwrite(tb, here(lolaDir, paste0("LOLA_", name, "UP_ATAC.tsv")), sep = "\t")
# NO
result <- runLOLA(anchor.no, anchor.all, lolaDB)
tb <- as_tibble(result)
fwrite(tb, here(lolaDir, paste0("LOLA_", name, "NO_ATAC.tsv")), sep = "\t")
# UPNO
result <- runLOLA(anchor.upno, anchor.all, lolaDB)
tb <- as_tibble(result)
fwrite(tb, here(lolaDir, paste0("LOLA_", name, "UPNO_ATAC.tsv")), sep = "\t")
# DOWN
result <- runLOLA(anchor.down, anchor.all, lolaDB)
tb <- as_tibble(result)
fwrite(tb, here(lolaDir, paste0("LOLA_", name, "DOWN_ATAC.tsv")), sep = "\t")
### Visualizing p-value and OR
alpha <- 0.05
tb.up <- fread(here(lolaDir, paste0("LOLA_", name, "UP_ATAC.tsv"))) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = "UP") %>%
filter(str_to_lower(cellType) == "embryonic stem cell") %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, paste0("LOLA_", name, "NO_ATAC.tsv"))) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = "NO") %>%
filter(str_to_lower(cellType) == "embryonic stem cell") %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.upno <- fread(here(lolaDir, paste0("LOLA_", name, "UPNO_ATAC.tsv"))) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = "UP/NO") %>%
filter(str_to_lower(cellType) == "embryonic stem cell") %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.upno %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, paste0("LOLA_", name, "UPNO_ATAC_pub.tsv")), sep = "\t")
tb.down <- fread(here(lolaDir, paste0("LOLA_", name, "DOWN_ATAC.tsv"))) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = "DOWN") %>%
filter(str_to_lower(cellType) == "embryonic stem cell") %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.down %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, paste0("LOLA_", name, "DOWN_ATAC__pub.tsv")), sep = "\t")
temp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.upno, temp.down)
order <- c((temp %>% dplyr::filter(group == "UP/NO") %>% arrange(desc(oddsRatio)))$target,
(temp %>% dplyr::filter(group == "DOWN") %>% arrange(desc(oddsRatio)))$target)
order <- unique(order)
temp$target <- factor(temp$target, levels = rev(order))
temp$group <- factor(temp$group, levels = c("UP/NO", "DOWN"))
targetList <- c("POLR2A", "CTR9", "ARR4", "ELL2",
"MED1", "MED12", "TBP", "TAF1", "TAF2",
"ESRRB", "KLF4", "NANOG", "POU5F1", "SOX2", "MYCN", "GASTA4", "STAT3", "E2F1", "YY1",
"P300", "DPY30", "RBBP5", "KDM2B", "KDM4B", "KDM6B", "TET1",
"CBX7", "RING1B", "RNF2", "EZH2", "SUZ12", "JARID2", "MTF2", "PHF19")
temp <- temp %>% dplyr::filter(target %in% targetList)
temp$target <- factor(temp$target, levels = rev(targetList))
qValueLogMax <- 50
temp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))
p <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = "white", high = "#CB333A",
limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = "#000000"
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = "#000000"
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = "#000000",
size = lineThick*mmToLineUnit,
lineend = "square"
),
axis.ticks = element_line(
color = "#000000",
size = lineThick*mmToLineUnit,
lineend = "square"
),
panel.background = element_rect(fill = "transparent"),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
figDir <- here("../figure/loop_analysis")
fileName <- paste0("LOLA_", name)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.9)*mmToInch
png(here(figDir, paste0(fileName, ".png")), res = 600, unit = "in", height = height, width = width)
print(p)
dev.off()
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjM1puYkdsMFpTaG9aWEpsS0dacFowUnBjaXdnY0dGemRHVXdLR1pwYkdWT1lXMWxMQ0JjSWk1emRtZGNJaWtwTENBZ2FHVnBaMmgwSUQwZ2FHVnBaMmgwTENCM2FXUjBhQ0E5SUhkcFpIUm9LVnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbnN2Z2xpdGUoaGVyZShmaWdEaXIsIHBhc3RlMChmaWxlTmFtZSwgXCIuc3ZnXCIpKSwgIGhlaWdodCA9IGhlaWdodCwgd2lkdGggPSB3aWR0aClcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3ZnbGl0ZShoZXJlKGZpZ0RpciwgcGFzdGUwKGZpbGVOYW1lLCBcIi5zdmdcIikpLCAgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxuYGBgIn0= -->
```r
svglite(here(figDir, paste0(fileName, ".svg")), height = height, width = width)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjSEpwYm5Rb2NDbGNibVJsZGk1dlptWW9LVnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbnByaW50KHApXG5kZXYub2ZmKClcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucHJpbnQocClcbmRldi5vZmYoKVxuYGBgIn0= -->
```r
print(p)
dev.off()
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### [FIG] Distance vs OOE plot per group
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG53aWR0aCA8LSBwYW5lbFNpemUoMi41KSptbVRvSW5jaFxuaGVpZ2h0IDwtIHBhbmVsU2l6ZSgxLjc1KSptbVRvSW5jaFxucG5nKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFwiLnBuZ1wiKSksIFxuICAgIHJlcyA9IDYwMCwgdW5pdHMgPSBcImluXCIsIHdpZHRoID0gd2lkdGgsIGhlaWdodCA9IGhlaWdodClcbnByaW50KHApXG5kZXYub2ZmKClcblxuYGBgIn0= -->
```r
width <- panelSize(2.5)*mmToInch
height <- panelSize(1.75)*mmToInch
png(here(figDir, paste0(fileName, ".png")),
res = 600, units = "in", width = width, height = height)
print(p)
dev.off()
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjM1puYkdsMFpTaG9aWEpsS0dacFowUnBjaXdnY0dGemRHVXdLR1pwYkdWT1lXMWxMQ0JjSWk1emRtZGNJaWtwTENCY2JpQWdJQ0FnSUNBZ2QybGtkR2dnUFNCM2FXUjBhQ3dnYUdWcFoyaDBJRDBnYUdWcFoyaDBLVnh1Y0hKcGJuUW9jQ2xjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbnN2Z2xpdGUoaGVyZShmaWdEaXIsIHBhc3RlMChmaWxlTmFtZSwgXCIuc3ZnXCIpKSwgXG4gICAgICAgIHdpZHRoID0gd2lkdGgsIGhlaWdodCA9IGhlaWdodClcbnByaW50KHApXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3ZnbGl0ZShoZXJlKGZpZ0RpciwgcGFzdGUwKGZpbGVOYW1lLCBcIi5zdmdcIikpLCBcbiAgICAgICAgd2lkdGggPSB3aWR0aCwgaGVpZ2h0ID0gaGVpZ2h0KVxucHJpbnQocClcbmBgYCJ9 -->
```r
svglite(here(figDir, paste0(fileName, ".svg")),
width = width, height = height)
print(p)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVaR1YyTG05bVppZ3BYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmRldi5vZmYoKVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGV2Lm9mZigpXG5gYGAifQ== -->
```r
dev.off()
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## EXPLORATORY
### Chromosight scores
#### Scatterplots
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
alpha <- 0.05
fcCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \chromosight\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_simple_ensemblList.tsv\))) %>%
dplyr::mutate(diff = score_dTAG - score_DMSO)
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_score_simpleAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_score_simpleAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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```r
```r
alpha <- 0.05
fcCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \chromosight\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_simple_ensemblList.tsv\))) %>%
dplyr::mutate(diff = score_dTAG - score_DMSO)
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_score_simpleAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_score_simpleAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### RNA-seq loopscore scatterplot
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
alpha <- 0.05
fcCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \chromosight\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_complex_ensemblList.tsv\))) %>%
dplyr::mutate(diff = score_dTAG - score_DMSO)
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_score_complexAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_score_complexAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
alpha <- 0.05
fcCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \chromosight\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_complex_ensemblList.tsv\))) %>%
dplyr::mutate(diff = score_dTAG - score_DMSO)
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_score_complexAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_score_complexAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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-->\n\n```r\nplot_loop_scores <- function(df, col_x, col_y, annoList,\n                             name, title, figDir,\n                             diffCutoff = 0.5, density_n = 200,\n                             xmin = -0.5, xmax = 1,\n                             ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  # Scatterplot\n  df2 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps),\n      density = get_density(lx, ly, n = density_n)\n    ) %>%\n    arrange(density)\n  \n  p1 <- ggplot(df2, aes(x = lx, y = ly, color = density)) +\n    geom_point() +\n    scale_color_viridis() +\n    scale_x_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_x_q), \\)\\)\n    ) +\n    scale_y_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_y_q), \\)\\)\n    ) +\n    coord_fixed() +\n    geom_abline(slope = 1, intercept = 0,\n                colour = \\grey50\\, linetype = \\dashed\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_abline(slope = 1, intercept = -(2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = (2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    theme_classic() +\n    labs(x = quo_name(col_x_q),\n         y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = 3.5, height = 3, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # Pie chart\n  df3 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    ) %>%\n    mutate(diff = ly - lx) %>%\n    mutate(updown = ifelse(diff > diffCutoff, \\UP\\,\n                           ifelse(diff < -diffCutoff, \\DOWN\\, \\NO\\)))\n  df3$updown <- factor(df3$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  pie_df <- df3 %>%\n    dplyr::count(updown) %>%\n    mutate(pct   = n / sum(n) * 100,\n           label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct,1), \\%)\\))\n\n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(aes(label = label),\n              position = position_stack(vjust = 0.5),\n              size     = fontSizeM/2) +\n    theme_void() +   theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(strong_red, no_grey, strong_blue))\n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n}\n\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_chromosight.tsv\\))\n\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_dTAGvDMSO\\,\n                 title = \\chromo_ooe_all_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_dTAGvDMSO\\,\n                 title = \\chromo_ooe_str_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_dTAGvDMSO\\,\n                 title = \\chromo_ooe_str2_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_dTAGvDMSO\\,\n                 title = \\chromo_ooe_reg_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_dTAGvDMSO\\,\n                 title = \\chromo_ooe_regStr_dTAGvDMSO\\, figDir)\n\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_A485vDMSO\\,\n                 title = \\chromo_ooe_all_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_A485vDMSO\\,\n                 title = \\chromo_ooe_str_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_A485vDMSO\\,\n                 title = \\chromo_ooe_str2_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_A485vDMSO\\,\n                 title = \\chromo_ooe_reg_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_A485vDMSO\\,\n                 title = \\chromo_ooe_regStr_A485vDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_all_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_str_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_str2_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_reg_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_regStr_EpidTAGvEpiDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_AIDvUT\\,\n                 title = \\chromo_ooe_all_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_AIDvUT\\,\n                 title = \\chromo_ooe_str_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_AIDvUT\\,\n                 title = \\chromo_ooe_str2_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_AIDvUT\\,\n                 title = \\chromo_ooe_reg_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_AIDvUT\\,\n                 title = \\chromo_ooe_regStr_AIDvUT\\, figDir)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
plot_loop_scores <- function(df, col_x, col_y, annoList,
name, title, figDir,
diffCutoff = 0.5, density_n = 200,
xmin = -0.5, xmax = 1,
ymin = -0.5, ymax = 1) {
col_x_q <- enquo(col_x)
col_y_q <- enquo(col_y)
# Scatterplot
df2 <- df %>%
filter(Anno_simple %in% annoList) %>%
select(id, x = !!col_x_q, y = !!col_y_q) %>%
mutate(
minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),
eps = minpos * 0.1,
lx = log2(x + eps),
ly = log2(y + eps),
density = get_density(lx, ly, n = density_n)
) %>%
arrange(density)
p1 <- ggplot(df2, aes(x = lx, y = ly, color = density)) +
geom_point() +
scale_color_viridis() +
scale_x_continuous(
name = paste0(\log2(\, quo_name(col_x_q), \)\)
) +
scale_y_continuous(
name = paste0(\log2(\, quo_name(col_y_q), \)\)
) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0,
colour = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, colour = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, colour = \grey\) +
geom_abline(slope = 1, intercept = -(2**diffCutoff),
colour = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = (2**diffCutoff),
colour = \grey\, linetype = \dotted\) +
theme_classic() +
labs(x = quo_name(col_x_q),
y = quo_name(col_y_q)) +
ggtitle(title)
fileName <- paste0(\scatterplot_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
png(here(figDir, paste0(fileName, \.png\)),
width = 3.5, height = 3, units = \in\, res = 600)
print(p1)
dev.off()
# Pie chart
df3 <- df %>%
filter(Anno_simple %in% annoList) %>%
select(id, x = !!col_x_q, y = !!col_y_q) %>%
mutate(
minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),
eps = minpos * 0.1,
lx = log2(x + eps),
ly = log2(y + eps)
) %>%
mutate(diff = ly - lx) %>%
mutate(updown = ifelse(diff > diffCutoff, \UP\,
ifelse(diff < -diffCutoff, \DOWN\, \NO\)))
df3$updown <- factor(df3$updown, c(\UP\, \NO\, \DOWN\))
pie_df <- df3 %>%
dplyr::count(updown) %>%
mutate(pct = n / sum(n) * 100,
label = paste0(updown, \\n\, n, \\n(\, round(pct,1), \%)\))
p2 <- ggplot(pie_df, aes(x = \\, y = n, fill = updown)) +
geom_col(color = \white\) +
coord_polar(theta = \y\) +
geom_text(aes(label = label),
position = position_stack(vjust = 0.5),
size = fontSizeM/2) +
theme_void() + theme(legend.position = \none\) +
scale_fill_manual(values = c(strong_red, no_grey, strong_blue))
fileName <- here(figDir, paste0(\piechart_\, name))
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
svglite(paste0(fileName, \.svg\), width = width, height = height)
print(p2)
dev.off()
}
commonLoopDir <- here(\../data/loop_analysis\)
figDir <- here(\../figure/loop_analysis\)
dir.create(figDir, showWarnings = FALSE, recursive = TRUE)
data <- fread(here(commonLoopDir, \loopScore_chromosight.tsv\))
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_ooe_all_dTAGvDMSO\,
title = \chromo_ooe_all_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\),
name = \chromosight_ooe_str_dTAGvDMSO\,
title = \chromo_ooe_str_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\, \S-X\),
name = \chromosight_ooe_str2_dTAGvDMSO\,
title = \chromo_ooe_str2_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \E-E\),
name = \chromosight_ooe_reg_dTAGvDMSO\,
title = \chromo_ooe_reg_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-S\, \E-S\),
name = \chromosight_ooe_regStr_dTAGvDMSO\,
title = \chromo_ooe_regStr_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_ooe_all_A485vDMSO\,
title = \chromo_ooe_all_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\),
name = \chromosight_ooe_str_A485vDMSO\,
title = \chromo_ooe_str_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\, \S-X\),
name = \chromosight_ooe_str2_A485vDMSO\,
title = \chromo_ooe_str2_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \E-E\),
name = \chromosight_ooe_reg_A485vDMSO\,
title = \chromo_ooe_reg_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-S\, \E-S\),
name = \chromosight_ooe_regStr_A485vDMSO\,
title = \chromo_ooe_regStr_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_ooe_all_EpidTAGvEpiDMSO\,
title = \chromo_ooe_all_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\),
name = \chromosight_ooe_str_EpidTAGvEpiDMSO\,
title = \chromo_ooe_str_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\, \S-X\),
name = \chromosight_ooe_str2_EpidTAGvEpiDMSO\,
title = \chromo_ooe_str2_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \E-E\),
name = \chromosight_ooe_reg_EpidTAGvEpiDMSO\,
title = \chromo_ooe_reg_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-S\, \E-S\),
name = \chromosight_ooe_regStr_EpidTAGvEpiDMSO\,
title = \chromo_ooe_regStr_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_ooe_all_AIDvUT\,
title = \chromo_ooe_all_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\),
name = \chromosight_ooe_str_AIDvUT\,
title = \chromo_ooe_str_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\, \S-X\),
name = \chromosight_ooe_str2_AIDvUT\,
title = \chromo_ooe_str2_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \E-E\),
name = \chromosight_ooe_reg_AIDvUT\,
title = \chromo_ooe_reg_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-S\, \E-S\),
name = \chromosight_ooe_regStr_AIDvUT\,
title = \chromo_ooe_regStr_AIDvUT\, figDir)
<!-- rnb-output-end -->
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-->\n\n```r\n```r\nplot_loop_scores <- function(df, col_x, col_y, annoList,\n                             name, title, figDir,\n                             diffCutoff = 0.5, density_n = 200,\n                             xmin = -0.5, xmax = 1,\n                             ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  # Scatterplot\n  df2 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps),\n      density = get_density(lx, ly, n = density_n)\n    ) %>%\n    arrange(density)\n  \n  p1 <- ggplot(df2, aes(x = lx, y = ly, color = density)) +\n    geom_point() +\n    scale_color_viridis() +\n    scale_x_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_x_q), \\)\\)\n    ) +\n    scale_y_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_y_q), \\)\\)\n    ) +\n    coord_fixed() +\n    geom_abline(slope = 1, intercept = 0,\n                colour = \\grey50\\, linetype = \\dashed\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_abline(slope = 1, intercept = -(2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = (2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    theme_classic() +\n    labs(x = quo_name(col_x_q),\n         y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = 3.5, height = 3, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # Pie chart\n  df3 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    ) %>%\n    mutate(diff = ly - lx) %>%\n    mutate(updown = ifelse(diff > diffCutoff, \\UP\\,\n                           ifelse(diff < -diffCutoff, \\DOWN\\, \\NO\\)))\n  df3$updown <- factor(df3$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  pie_df <- df3 %>%\n    dplyr::count(updown) %>%\n    mutate(pct   = n / sum(n) * 100,\n           label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct,1), \\%)\\))\n\n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(aes(label = label),\n              position = position_stack(vjust = 0.5),\n              size     = fontSizeM/2) +\n    theme_void() +   theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(strong_red, no_grey, strong_blue))\n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n}\n\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_chromosight.tsv\\))\n\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_dTAGvDMSO\\,\n                 title = \\chromo_ooe_all_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_dTAGvDMSO\\,\n                 title = \\chromo_ooe_str_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_dTAGvDMSO\\,\n                 title = \\chromo_ooe_str2_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_dTAGvDMSO\\,\n                 title = \\chromo_ooe_reg_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_dTAGvDMSO\\,\n                 title = \\chromo_ooe_regStr_dTAGvDMSO\\, figDir)\n\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_A485vDMSO\\,\n                 title = \\chromo_ooe_all_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_A485vDMSO\\,\n                 title = \\chromo_ooe_str_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_A485vDMSO\\,\n                 title = \\chromo_ooe_str2_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_A485vDMSO\\,\n                 title = \\chromo_ooe_reg_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_A485vDMSO\\,\n                 title = \\chromo_ooe_regStr_A485vDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_all_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_str_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_str2_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_reg_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_regStr_EpidTAGvEpiDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_AIDvUT\\,\n                 title = \\chromo_ooe_all_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_AIDvUT\\,\n                 title = \\chromo_ooe_str_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_AIDvUT\\,\n                 title = \\chromo_ooe_str2_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_AIDvUT\\,\n                 title = \\chromo_ooe_reg_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_AIDvUT\\,\n                 title = \\chromo_ooe_regStr_AIDvUT\\, figDir)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nplot_loop_scores <- function(df, col_x, col_y, annoList,\n                             name, title, figDir,\n                             diffCutoff = 0.5, density_n = 200,\n                             xmin = -0.5, xmax = 1,\n                             ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  # Scatterplot\n  df2 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps),\n      density = get_density(lx, ly, n = density_n)\n    ) %>%\n    arrange(density)\n  \n  p1 <- ggplot(df2, aes(x = lx, y = ly, color = density)) +\n    geom_point() +\n    scale_color_viridis() +\n    scale_x_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_x_q), \\)\\)\n    ) +\n    scale_y_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_y_q), \\)\\)\n    ) +\n    coord_fixed() +\n    geom_abline(slope = 1, intercept = 0,\n                colour = \\grey50\\, linetype = \\dashed\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_abline(slope = 1, intercept = -(2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = (2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    theme_classic() +\n    labs(x = quo_name(col_x_q),\n         y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = 3.5, height = 3, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # Pie chart\n  df3 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    ) %>%\n    mutate(diff = ly - lx) %>%\n    mutate(updown = ifelse(diff > diffCutoff, \\UP\\,\n                           ifelse(diff < -diffCutoff, \\DOWN\\, \\NO\\)))\n  df3$updown <- factor(df3$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  pie_df <- df3 %>%\n    dplyr::count(updown) %>%\n    mutate(pct   = n / sum(n) * 100,\n           label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct,1), \\%)\\))\n\n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(aes(label = label),\n              position = position_stack(vjust = 0.5),\n              size     = fontSizeM/2) +\n    theme_void() +   theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(strong_red, no_grey, strong_blue))\n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n}\n\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_chromosight.tsv\\))\n\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_dTAGvDMSO\\,\n                 title = \\chromo_ooe_all_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_dTAGvDMSO\\,\n                 title = \\chromo_ooe_str_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_dTAGvDMSO\\,\n                 title = \\chromo_ooe_str2_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_dTAGvDMSO\\,\n                 title = \\chromo_ooe_reg_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_dTAGvDMSO\\,\n                 title = \\chromo_ooe_regStr_dTAGvDMSO\\, figDir)\n\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_A485vDMSO\\,\n                 title = \\chromo_ooe_all_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_A485vDMSO\\,\n                 title = \\chromo_ooe_str_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_A485vDMSO\\,\n                 title = \\chromo_ooe_str2_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_A485vDMSO\\,\n                 title = \\chromo_ooe_reg_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_A485vDMSO\\,\n                 title = \\chromo_ooe_regStr_A485vDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_all_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_str_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_str2_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_reg_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_EpidTAGvEpiDMSO\\,\n                 title = \\chromo_ooe_regStr_EpidTAGvEpiDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\chromosight_ooe_all_AIDvUT\\,\n                 title = \\chromo_ooe_all_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\),\n                 name = \\chromosight_ooe_str_AIDvUT\\,\n                 title = \\chromo_ooe_str_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\, \\S-X\\),\n                 name = \\chromosight_ooe_str2_AIDvUT\\,\n                 title = \\chromo_ooe_str2_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\chromosight_ooe_reg_AIDvUT\\,\n                 title = \\chromo_ooe_reg_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-S\\, \\E-S\\),\n                 name = \\chromosight_ooe_regStr_AIDvUT\\,\n                 title = \\chromo_ooe_regStr_AIDvUT\\, figDir)\n\n\n```\n```"} -->
```r
```r
plot_loop_scores <- function(df, col_x, col_y, annoList,
name, title, figDir,
diffCutoff = 0.5, density_n = 200,
xmin = -0.5, xmax = 1,
ymin = -0.5, ymax = 1) {
col_x_q <- enquo(col_x)
col_y_q <- enquo(col_y)
# Scatterplot
df2 <- df %>%
filter(Anno_simple %in% annoList) %>%
select(id, x = !!col_x_q, y = !!col_y_q) %>%
mutate(
minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),
eps = minpos * 0.1,
lx = log2(x + eps),
ly = log2(y + eps),
density = get_density(lx, ly, n = density_n)
) %>%
arrange(density)
p1 <- ggplot(df2, aes(x = lx, y = ly, color = density)) +
geom_point() +
scale_color_viridis() +
scale_x_continuous(
name = paste0(\log2(\, quo_name(col_x_q), \)\)
) +
scale_y_continuous(
name = paste0(\log2(\, quo_name(col_y_q), \)\)
) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0,
colour = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, colour = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, colour = \grey\) +
geom_abline(slope = 1, intercept = -(2**diffCutoff),
colour = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = (2**diffCutoff),
colour = \grey\, linetype = \dotted\) +
theme_classic() +
labs(x = quo_name(col_x_q),
y = quo_name(col_y_q)) +
ggtitle(title)
fileName <- paste0(\scatterplot_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
png(here(figDir, paste0(fileName, \.png\)),
width = 3.5, height = 3, units = \in\, res = 600)
print(p1)
dev.off()
# Pie chart
df3 <- df %>%
filter(Anno_simple %in% annoList) %>%
select(id, x = !!col_x_q, y = !!col_y_q) %>%
mutate(
minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),
eps = minpos * 0.1,
lx = log2(x + eps),
ly = log2(y + eps)
) %>%
mutate(diff = ly - lx) %>%
mutate(updown = ifelse(diff > diffCutoff, \UP\,
ifelse(diff < -diffCutoff, \DOWN\, \NO\)))
df3$updown <- factor(df3$updown, c(\UP\, \NO\, \DOWN\))
pie_df <- df3 %>%
dplyr::count(updown) %>%
mutate(pct = n / sum(n) * 100,
label = paste0(updown, \\n\, n, \\n(\, round(pct,1), \%)\))
p2 <- ggplot(pie_df, aes(x = \\, y = n, fill = updown)) +
geom_col(color = \white\) +
coord_polar(theta = \y\) +
geom_text(aes(label = label),
position = position_stack(vjust = 0.5),
size = fontSizeM/2) +
theme_void() + theme(legend.position = \none\) +
scale_fill_manual(values = c(strong_red, no_grey, strong_blue))
fileName <- here(figDir, paste0(\piechart_\, name))
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
svglite(paste0(fileName, \.svg\), width = width, height = height)
print(p2)
dev.off()
}
commonLoopDir <- here(\../data/loop_analysis\)
figDir <- here(\../figure/loop_analysis\)
dir.create(figDir, showWarnings = FALSE, recursive = TRUE)
data <- fread(here(commonLoopDir, \loopScore_chromosight.tsv\))
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_ooe_all_dTAGvDMSO\,
title = \chromo_ooe_all_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\),
name = \chromosight_ooe_str_dTAGvDMSO\,
title = \chromo_ooe_str_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\, \S-X\),
name = \chromosight_ooe_str2_dTAGvDMSO\,
title = \chromo_ooe_str2_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \E-E\),
name = \chromosight_ooe_reg_dTAGvDMSO\,
title = \chromo_ooe_reg_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-S\, \E-S\),
name = \chromosight_ooe_regStr_dTAGvDMSO\,
title = \chromo_ooe_regStr_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_ooe_all_A485vDMSO\,
title = \chromo_ooe_all_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\),
name = \chromosight_ooe_str_A485vDMSO\,
title = \chromo_ooe_str_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\, \S-X\),
name = \chromosight_ooe_str2_A485vDMSO\,
title = \chromo_ooe_str2_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \E-E\),
name = \chromosight_ooe_reg_A485vDMSO\,
title = \chromo_ooe_reg_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-S\, \E-S\),
name = \chromosight_ooe_regStr_A485vDMSO\,
title = \chromo_ooe_regStr_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_ooe_all_EpidTAGvEpiDMSO\,
title = \chromo_ooe_all_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\),
name = \chromosight_ooe_str_EpidTAGvEpiDMSO\,
title = \chromo_ooe_str_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\, \S-X\),
name = \chromosight_ooe_str2_EpidTAGvEpiDMSO\,
title = \chromo_ooe_str2_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \E-E\),
name = \chromosight_ooe_reg_EpidTAGvEpiDMSO\,
title = \chromo_ooe_reg_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-S\, \E-S\),
name = \chromosight_ooe_regStr_EpidTAGvEpiDMSO\,
title = \chromo_ooe_regStr_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \chromosight_ooe_all_AIDvUT\,
title = \chromo_ooe_all_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\),
name = \chromosight_ooe_str_AIDvUT\,
title = \chromo_ooe_str_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\, \S-X\),
name = \chromosight_ooe_str2_AIDvUT\,
title = \chromo_ooe_str2_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \E-E\),
name = \chromosight_ooe_reg_AIDvUT\,
title = \chromo_ooe_reg_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-S\, \E-S\),
name = \chromosight_ooe_regStr_AIDvUT\,
title = \chromo_ooe_regStr_AIDvUT\, figDir)
```
```
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<!-- rnb-text-begin -->
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<!-- rnb-output-begin 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 -->
loadLoopAnnoData <- function(fileName){
data <- as_tibble(fread(fileName)) %>%
dplyr::mutate(gene = strsplit(gene, '\\|'))
return(data)
}
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubG9hZExvb3BBbm5vRGF0YSA8LSBmdW5jdGlvbihmaWxlTmFtZSl7XG4gIGRhdGEgPC0gYXNfdGliYmxlKGZyZWFkKGZpbGVOYW1lKSkgJT4lXG4gICAgZHBseXI6Om11dGF0ZShnZW5lID0gc3Ryc3BsaXQoZ2VuZSwgJ1xcXFx8JykpXG4gIHJldHVybihkYXRhKVxufVxuYGBgXG5gYGAifQ== -->
```r
```r
loadLoopAnnoData <- function(fileName){
data <- as_tibble(fread(fileName)) %>%
dplyr::mutate(gene = strsplit(gene, '\\|'))
return(data)
}
```
```
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### Obs/exp score
#### Scatterplot - Chromosight
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\chromosight\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_simple_ensemblList.tsv\\))) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_simpleAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_simpleAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \chromosight\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_simple_ensemblList.tsv\))) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_simpleAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_simpleAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\chromosight\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_simple_ensemblList.tsv\\))) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_simpleAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_simpleAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \chromosight\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_simple_ensemblList.tsv\))) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_simpleAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_simpleAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### RNA-seq loopscore scatterplot
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\chromosight\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_complex_ensemblList.tsv\\))) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_complexAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_complexAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \chromosight\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_complex_ensemblList.tsv\))) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_complexAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_complexAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\chromosight\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_complex_ensemblList.tsv\\))) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_complexAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_complexAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \chromosight\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_complex_ensemblList.tsv\))) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_complexAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_complexAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_hicdcp.tsv\\))\n\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\hicdcp_ooe_all_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_all_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\),\n                 name = \\hicdcp_ooe_str_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_str_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\hicdcp_ooe_str2_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_str2_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\hicdcp_ooe_reg_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_reg_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\hicdcp_ooe_regStr_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_regStr_dTAGvDMSO\\, figDir)\n\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\hicdcp_ooe_all_A485vDMSO\\,\n                 title = \\hicdcp_ooe_all_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\),\n                 name = \\hicdcp_ooe_str_A485vDMSO\\,\n                 title = \\hicdcp_ooe_str_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\, \\S-X\\),\n                 name = \\hicdcp_ooe_str2_A485vDMSO\\,\n                 title = \\hicdcp_ooe_str2_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\hicdcp_ooe_reg_A485vDMSO\\,\n                 title = \\hicdcp_ooe_reg_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-S\\, \\E-S\\),\n                 name = \\hicdcp_ooe_regStr_A485vDMSO\\,\n                 title = \\hicdcp_ooe_regStr_A485vDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\hicdcp_ooe_all_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_all_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\),\n                 name = \\hicdcp_ooe_str_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_str_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\hicdcp_ooe_str2_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_str2_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\hicdcp_ooe_reg_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_reg_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\hicdcp_ooe_regStr_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_regStr_EpidTAGvEpiDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\hicdcp_ooe_all_AIDvUT\\,\n                 title = \\hicdcp_ooe_all_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\),\n                 name = \\hicdcp_ooe_str_AIDvUT\\,\n                 title = \\hicdcp_ooe_str_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\, \\S-X\\),\n                 name = \\hicdcp_ooe_str2_AIDvUT\\,\n                 title = \\hicdcp_ooe_str2_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\hicdcp_ooe_reg_AIDvUT\\,\n                 title = \\hicdcp_ooe_reg_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-S\\, \\E-S\\),\n                 name = \\hicdcp_ooe_regStr_AIDvUT\\,\n                 title = \\hicdcp_ooe_regStr_AIDvUT\\, figDir)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
commonLoopDir <- here(\../data/loop_analysis\)
figDir <- here(\../figure/loop_analysis\)
dir.create(figDir, showWarnings = FALSE, recursive = TRUE)
data <- fread(here(commonLoopDir, \loopScore_hicdcp.tsv\))
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \hicdcp_ooe_all_dTAGvDMSO\,
title = \hicdcp_ooe_all_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\),
name = \hicdcp_ooe_str_dTAGvDMSO\,
title = \hicdcp_ooe_str_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\, \S-X\),
name = \hicdcp_ooe_str2_dTAGvDMSO\,
title = \hicdcp_ooe_str2_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \E-E\),
name = \hicdcp_ooe_reg_dTAGvDMSO\,
title = \hicdcp_ooe_reg_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-S\, \E-S\),
name = \hicdcp_ooe_regStr_dTAGvDMSO\,
title = \hicdcp_ooe_regStr_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \hicdcp_ooe_all_A485vDMSO\,
title = \hicdcp_ooe_all_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\),
name = \hicdcp_ooe_str_A485vDMSO\,
title = \hicdcp_ooe_str_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\, \S-X\),
name = \hicdcp_ooe_str2_A485vDMSO\,
title = \hicdcp_ooe_str2_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \E-E\),
name = \hicdcp_ooe_reg_A485vDMSO\,
title = \hicdcp_ooe_reg_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-S\, \E-S\),
name = \hicdcp_ooe_regStr_A485vDMSO\,
title = \hicdcp_ooe_regStr_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \hicdcp_ooe_all_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_all_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\),
name = \hicdcp_ooe_str_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_str_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\, \S-X\),
name = \hicdcp_ooe_str2_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_str2_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \E-E\),
name = \hicdcp_ooe_reg_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_reg_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-S\, \E-S\),
name = \hicdcp_ooe_regStr_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_regStr_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \hicdcp_ooe_all_AIDvUT\,
title = \hicdcp_ooe_all_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\),
name = \hicdcp_ooe_str_AIDvUT\,
title = \hicdcp_ooe_str_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\, \S-X\),
name = \hicdcp_ooe_str2_AIDvUT\,
title = \hicdcp_ooe_str2_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \E-E\),
name = \hicdcp_ooe_reg_AIDvUT\,
title = \hicdcp_ooe_reg_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-S\, \E-S\),
name = \hicdcp_ooe_regStr_AIDvUT\,
title = \hicdcp_ooe_regStr_AIDvUT\, figDir)
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_hicdcp.tsv\\))\n\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\hicdcp_ooe_all_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_all_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\),\n                 name = \\hicdcp_ooe_str_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_str_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\hicdcp_ooe_str2_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_str2_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\hicdcp_ooe_reg_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_reg_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\hicdcp_ooe_regStr_dTAGvDMSO\\,\n                 title = \\hicdcp_ooe_regStr_dTAGvDMSO\\, figDir)\n\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\hicdcp_ooe_all_A485vDMSO\\,\n                 title = \\hicdcp_ooe_all_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\),\n                 name = \\hicdcp_ooe_str_A485vDMSO\\,\n                 title = \\hicdcp_ooe_str_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\, \\S-X\\),\n                 name = \\hicdcp_ooe_str2_A485vDMSO\\,\n                 title = \\hicdcp_ooe_str2_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\hicdcp_ooe_reg_A485vDMSO\\,\n                 title = \\hicdcp_ooe_reg_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-S\\, \\E-S\\),\n                 name = \\hicdcp_ooe_regStr_A485vDMSO\\,\n                 title = \\hicdcp_ooe_regStr_A485vDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\hicdcp_ooe_all_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_all_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\),\n                 name = \\hicdcp_ooe_str_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_str_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\hicdcp_ooe_str2_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_str2_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\hicdcp_ooe_reg_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_reg_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\hicdcp_ooe_regStr_EpidTAGvEpiDMSO\\,\n                 title = \\hicdcp_ooe_regStr_EpidTAGvEpiDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\hicdcp_ooe_all_AIDvUT\\,\n                 title = \\hicdcp_ooe_all_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\),\n                 name = \\hicdcp_ooe_str_AIDvUT\\,\n                 title = \\hicdcp_ooe_str_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\, \\S-X\\),\n                 name = \\hicdcp_ooe_str2_AIDvUT\\,\n                 title = \\hicdcp_ooe_str2_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\hicdcp_ooe_reg_AIDvUT\\,\n                 title = \\hicdcp_ooe_reg_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-S\\, \\E-S\\),\n                 name = \\hicdcp_ooe_regStr_AIDvUT\\,\n                 title = \\hicdcp_ooe_regStr_AIDvUT\\, figDir)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
commonLoopDir <- here(\../data/loop_analysis\)
figDir <- here(\../figure/loop_analysis\)
dir.create(figDir, showWarnings = FALSE, recursive = TRUE)
data <- fread(here(commonLoopDir, \loopScore_hicdcp.tsv\))
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \hicdcp_ooe_all_dTAGvDMSO\,
title = \hicdcp_ooe_all_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\),
name = \hicdcp_ooe_str_dTAGvDMSO\,
title = \hicdcp_ooe_str_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\, \S-X\),
name = \hicdcp_ooe_str2_dTAGvDMSO\,
title = \hicdcp_ooe_str2_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \E-E\),
name = \hicdcp_ooe_reg_dTAGvDMSO\,
title = \hicdcp_ooe_reg_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-S\, \E-S\),
name = \hicdcp_ooe_regStr_dTAGvDMSO\,
title = \hicdcp_ooe_regStr_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \hicdcp_ooe_all_A485vDMSO\,
title = \hicdcp_ooe_all_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\),
name = \hicdcp_ooe_str_A485vDMSO\,
title = \hicdcp_ooe_str_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\, \S-X\),
name = \hicdcp_ooe_str2_A485vDMSO\,
title = \hicdcp_ooe_str2_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \E-E\),
name = \hicdcp_ooe_reg_A485vDMSO\,
title = \hicdcp_ooe_reg_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-S\, \E-S\),
name = \hicdcp_ooe_regStr_A485vDMSO\,
title = \hicdcp_ooe_regStr_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \hicdcp_ooe_all_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_all_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\),
name = \hicdcp_ooe_str_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_str_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\, \S-X\),
name = \hicdcp_ooe_str2_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_str2_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \E-E\),
name = \hicdcp_ooe_reg_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_reg_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-S\, \E-S\),
name = \hicdcp_ooe_regStr_EpidTAGvEpiDMSO\,
title = \hicdcp_ooe_regStr_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \hicdcp_ooe_all_AIDvUT\,
title = \hicdcp_ooe_all_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\),
name = \hicdcp_ooe_str_AIDvUT\,
title = \hicdcp_ooe_str_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\, \S-X\),
name = \hicdcp_ooe_str2_AIDvUT\,
title = \hicdcp_ooe_str2_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \E-E\),
name = \hicdcp_ooe_reg_AIDvUT\,
title = \hicdcp_ooe_reg_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-S\, \E-S\),
name = \hicdcp_ooe_regStr_AIDvUT\,
title = \hicdcp_ooe_regStr_AIDvUT\, figDir)
```
```
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\hicdcp\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_simple_ensemblList.tsv\\))) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_simpleAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_simpleAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \hicdcp\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_simple_ensemblList.tsv\))) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_simpleAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_simpleAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\hicdcp\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_simple_ensemblList.tsv\\))) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_simpleAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_simpleAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \hicdcp\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_simple_ensemblList.tsv\))) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_simpleAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_simpleAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-text-begin -->
#### Scatterplot - HiCDC+
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\hicdcp\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_complex_ensemblList.tsv\\))) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_complexAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_complexAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \hicdcp\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_complex_ensemblList.tsv\))) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_complexAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_complexAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\hicdcp\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_complex_ensemblList.tsv\\))) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_complexAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_complexAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \hicdcp\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_complex_ensemblList.tsv\))) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_complexAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_complexAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### RNA-seq loopscore scatterplot
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nplot_loop_scores <- function(df, col_x, col_y, annoList,\n                             name, title, figDir,\n                             diffCutoff = 0.5, density_n = 100,\n                             xmin = -0.5, xmax = 1,\n                             ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  # Scatterplot\n  df2 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    )\n  \n  p1 <- ggplot(df2, aes(x = lx, y = ly)) +\n    geom_point() +\n    scale_color_viridis() +\n    scale_x_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_x_q), \\)\\)\n    ) +\n    scale_y_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_y_q), \\)\\)\n    ) +\n    coord_fixed() +\n    geom_abline(slope = 1, intercept = 0,\n                colour = \\grey50\\, linetype = \\dashed\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_abline(slope = 1, intercept = -(2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = (2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    theme_classic() +\n    labs(x = quo_name(col_x_q),\n         y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = 3.5, height = 3, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # Pie chart\n  df3 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    ) %>%\n    mutate(diff = ly - lx) %>%\n    mutate(updown = ifelse(diff > diffCutoff, \\UP\\,\n                           ifelse(diff < -diffCutoff, \\DOWN\\, \\NO\\)))\n  df3$updown <- factor(df3$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  pie_df <- df3 %>%\n    dplyr::count(updown) %>%\n    mutate(pct   = n / sum(n) * 100,\n           label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct,1), \\%)\\))\n\n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(aes(label = label),\n              position = position_stack(vjust = 0.5),\n              size     = fontSizeM/2) +\n    theme_void() +   theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(strong_red, no_grey, strong_blue))\n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n}\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_Hansen.tsv\\))\ndata <- data %>% \n  filter(\n    !if_all(starts_with(\\ooe\\), ~ .x == 0)   # drop rows where *every* ooe* value is 0\n)\n\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_all_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_str_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_str2_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_reg_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_regStr_dTAGvDMSO\\, figDir)\n\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_A485vDMSO\\,\n                 title = \\Hansen_ooe_all_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_A485vDMSO\\,\n                 title = \\Hansen_ooe_str_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_A485vDMSO\\,\n                 title = \\Hansen_ooe_str2_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_A485vDMSO\\,\n                 title = \\Hansen_ooe_reg_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_A485vDMSO\\,\n                 title = \\Hansen_ooe_regStr_A485vDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_all_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_str_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_str2_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_reg_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_regStr_EpidTAGvEpiDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_AIDvUT\\,\n                 title = \\Hansen_ooe_all_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_AIDvUT\\,\n                 title = \\Hansen_ooe_str_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_AIDvUT\\,\n                 title = \\Hansen_ooe_str2_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_AIDvUT\\,\n                 title = \\Hansen_ooe_reg_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_AIDvUT\\,\n                 title = \\Hansen_ooe_regStr_AIDvUT\\, figDir)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
plot_loop_scores <- function(df, col_x, col_y, annoList,
name, title, figDir,
diffCutoff = 0.5, density_n = 100,
xmin = -0.5, xmax = 1,
ymin = -0.5, ymax = 1) {
col_x_q <- enquo(col_x)
col_y_q <- enquo(col_y)
# Scatterplot
df2 <- df %>%
filter(Anno_simple %in% annoList) %>%
select(id, x = !!col_x_q, y = !!col_y_q) %>%
mutate(
minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),
eps = minpos * 0.1,
lx = log2(x + eps),
ly = log2(y + eps)
)
p1 <- ggplot(df2, aes(x = lx, y = ly)) +
geom_point() +
scale_color_viridis() +
scale_x_continuous(
name = paste0(\log2(\, quo_name(col_x_q), \)\)
) +
scale_y_continuous(
name = paste0(\log2(\, quo_name(col_y_q), \)\)
) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0,
colour = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, colour = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, colour = \grey\) +
geom_abline(slope = 1, intercept = -(2**diffCutoff),
colour = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = (2**diffCutoff),
colour = \grey\, linetype = \dotted\) +
theme_classic() +
labs(x = quo_name(col_x_q),
y = quo_name(col_y_q)) +
ggtitle(title)
fileName <- paste0(\scatterplot_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
png(here(figDir, paste0(fileName, \.png\)),
width = 3.5, height = 3, units = \in\, res = 600)
print(p1)
dev.off()
# Pie chart
df3 <- df %>%
filter(Anno_simple %in% annoList) %>%
select(id, x = !!col_x_q, y = !!col_y_q) %>%
mutate(
minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),
eps = minpos * 0.1,
lx = log2(x + eps),
ly = log2(y + eps)
) %>%
mutate(diff = ly - lx) %>%
mutate(updown = ifelse(diff > diffCutoff, \UP\,
ifelse(diff < -diffCutoff, \DOWN\, \NO\)))
df3$updown <- factor(df3$updown, c(\UP\, \NO\, \DOWN\))
pie_df <- df3 %>%
dplyr::count(updown) %>%
mutate(pct = n / sum(n) * 100,
label = paste0(updown, \\n\, n, \\n(\, round(pct,1), \%)\))
p2 <- ggplot(pie_df, aes(x = \\, y = n, fill = updown)) +
geom_col(color = \white\) +
coord_polar(theta = \y\) +
geom_text(aes(label = label),
position = position_stack(vjust = 0.5),
size = fontSizeM/2) +
theme_void() + theme(legend.position = \none\) +
scale_fill_manual(values = c(strong_red, no_grey, strong_blue))
fileName <- here(figDir, paste0(\piechart_\, name))
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
svglite(paste0(fileName, \.svg\), width = width, height = height)
print(p2)
dev.off()
}
commonLoopDir <- here(\../data/loop_analysis\)
figDir <- here(\../figure/loop_analysis\)
dir.create(figDir, showWarnings = FALSE, recursive = TRUE)
data <- fread(here(commonLoopDir, \loopScore_Hansen.tsv\))
data <- data %>%
filter(
!if_all(starts_with(\ooe\), ~ .x == 0) # drop rows where *every* ooe* value is 0
)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \Hansen_ooe_all_dTAGvDMSO\,
title = \Hansen_ooe_all_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\),
name = \Hansen_ooe_str_dTAGvDMSO\,
title = \Hansen_ooe_str_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\, \S-X\),
name = \Hansen_ooe_str2_dTAGvDMSO\,
title = \Hansen_ooe_str2_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \E-E\),
name = \Hansen_ooe_reg_dTAGvDMSO\,
title = \Hansen_ooe_reg_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-S\, \E-S\),
name = \Hansen_ooe_regStr_dTAGvDMSO\,
title = \Hansen_ooe_regStr_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \Hansen_ooe_all_A485vDMSO\,
title = \Hansen_ooe_all_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\),
name = \Hansen_ooe_str_A485vDMSO\,
title = \Hansen_ooe_str_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\, \S-X\),
name = \Hansen_ooe_str2_A485vDMSO\,
title = \Hansen_ooe_str2_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \E-E\),
name = \Hansen_ooe_reg_A485vDMSO\,
title = \Hansen_ooe_reg_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-S\, \E-S\),
name = \Hansen_ooe_regStr_A485vDMSO\,
title = \Hansen_ooe_regStr_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \Hansen_ooe_all_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_all_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\),
name = \Hansen_ooe_str_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_str_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\, \S-X\),
name = \Hansen_ooe_str2_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_str2_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \E-E\),
name = \Hansen_ooe_reg_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_reg_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-S\, \E-S\),
name = \Hansen_ooe_regStr_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_regStr_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \Hansen_ooe_all_AIDvUT\,
title = \Hansen_ooe_all_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\),
name = \Hansen_ooe_str_AIDvUT\,
title = \Hansen_ooe_str_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\, \S-X\),
name = \Hansen_ooe_str2_AIDvUT\,
title = \Hansen_ooe_str2_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \E-E\),
name = \Hansen_ooe_reg_AIDvUT\,
title = \Hansen_ooe_reg_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-S\, \E-S\),
name = \Hansen_ooe_regStr_AIDvUT\,
title = \Hansen_ooe_regStr_AIDvUT\, figDir)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nplot_loop_scores <- function(df, col_x, col_y, annoList,\n                             name, title, figDir,\n                             diffCutoff = 0.5, density_n = 100,\n                             xmin = -0.5, xmax = 1,\n                             ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  # Scatterplot\n  df2 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    )\n  \n  p1 <- ggplot(df2, aes(x = lx, y = ly)) +\n    geom_point() +\n    scale_color_viridis() +\n    scale_x_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_x_q), \\)\\)\n    ) +\n    scale_y_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_y_q), \\)\\)\n    ) +\n    coord_fixed() +\n    geom_abline(slope = 1, intercept = 0,\n                colour = \\grey50\\, linetype = \\dashed\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_abline(slope = 1, intercept = -(2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = (2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    theme_classic() +\n    labs(x = quo_name(col_x_q),\n         y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = 3.5, height = 3, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # Pie chart\n  df3 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    ) %>%\n    mutate(diff = ly - lx) %>%\n    mutate(updown = ifelse(diff > diffCutoff, \\UP\\,\n                           ifelse(diff < -diffCutoff, \\DOWN\\, \\NO\\)))\n  df3$updown <- factor(df3$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  pie_df <- df3 %>%\n    dplyr::count(updown) %>%\n    mutate(pct   = n / sum(n) * 100,\n           label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct,1), \\%)\\))\n\n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(aes(label = label),\n              position = position_stack(vjust = 0.5),\n              size     = fontSizeM/2) +\n    theme_void() +   theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(strong_red, no_grey, strong_blue))\n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n}\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_Hansen.tsv\\))\ndata <- data %>% \n  filter(\n    !if_all(starts_with(\\ooe\\), ~ .x == 0)   # drop rows where *every* ooe* value is 0\n)\n\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_all_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_str_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_str2_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_reg_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_regStr_dTAGvDMSO\\, figDir)\n\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_A485vDMSO\\,\n                 title = \\Hansen_ooe_all_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_A485vDMSO\\,\n                 title = \\Hansen_ooe_str_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_A485vDMSO\\,\n                 title = \\Hansen_ooe_str2_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_A485vDMSO\\,\n                 title = \\Hansen_ooe_reg_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_A485vDMSO\\,\n                 title = \\Hansen_ooe_regStr_A485vDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_all_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_str_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_str2_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_reg_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_regStr_EpidTAGvEpiDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_AIDvUT\\,\n                 title = \\Hansen_ooe_all_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_AIDvUT\\,\n                 title = \\Hansen_ooe_str_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_AIDvUT\\,\n                 title = \\Hansen_ooe_str2_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_AIDvUT\\,\n                 title = \\Hansen_ooe_reg_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_AIDvUT\\,\n                 title = \\Hansen_ooe_regStr_AIDvUT\\, figDir)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nplot_loop_scores <- function(df, col_x, col_y, annoList,\n                             name, title, figDir,\n                             diffCutoff = 0.5, density_n = 100,\n                             xmin = -0.5, xmax = 1,\n                             ymin = -0.5, ymax = 1) {\n  col_x_q <- enquo(col_x)\n  col_y_q <- enquo(col_y)\n  \n  # Scatterplot\n  df2 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    )\n  \n  p1 <- ggplot(df2, aes(x = lx, y = ly)) +\n    geom_point() +\n    scale_color_viridis() +\n    scale_x_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_x_q), \\)\\)\n    ) +\n    scale_y_continuous(\n      name   = paste0(\\log2(\\, quo_name(col_y_q), \\)\\)\n    ) +\n    coord_fixed() +\n    geom_abline(slope = 1, intercept = 0,\n                colour = \\grey50\\, linetype = \\dashed\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, colour = \\grey\\) +\n    geom_abline(slope = 1, intercept = -(2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = (2**diffCutoff),\n                colour = \\grey\\, linetype = \\dotted\\) +\n    theme_classic() +\n    labs(x = quo_name(col_x_q),\n         y = quo_name(col_y_q)) +\n    ggtitle(title)\n  \n  fileName <- paste0(\\scatterplot_\\, name)\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      width = 3.5, height = 3, units = \\in\\, res = 600)\n  print(p1)\n  dev.off()\n  \n  # Pie chart\n  df3 <- df %>%\n    filter(Anno_simple %in% annoList) %>%\n    select(id, x = !!col_x_q, y = !!col_y_q) %>%\n    mutate(\n      minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),\n      eps    = minpos * 0.1,\n      lx     = log2(x + eps),\n      ly     = log2(y + eps)\n    ) %>%\n    mutate(diff = ly - lx) %>%\n    mutate(updown = ifelse(diff > diffCutoff, \\UP\\,\n                           ifelse(diff < -diffCutoff, \\DOWN\\, \\NO\\)))\n  df3$updown <- factor(df3$updown, c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  pie_df <- df3 %>%\n    dplyr::count(updown) %>%\n    mutate(pct   = n / sum(n) * 100,\n           label = paste0(updown, \\\\n\\, n, \\\\n(\\, round(pct,1), \\%)\\))\n\n  p2 <- ggplot(pie_df, aes(x = \\\\, y = n, fill = updown)) +\n    geom_col(color = \\white\\) +\n    coord_polar(theta = \\y\\) +\n    geom_text(aes(label = label),\n              position = position_stack(vjust = 0.5),\n              size     = fontSizeM/2) +\n    theme_void() +   theme(legend.position = \\none\\) +\n    scale_fill_manual(values = c(strong_red, no_grey, strong_blue))\n  fileName <- here(figDir, paste0(\\piechart_\\, name))\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(p2)\n  dev.off()\n}\n\ncommonLoopDir <- here(\\../data/loop_analysis\\)\nfigDir <- here(\\../figure/loop_analysis\\)\ndir.create(figDir, showWarnings = FALSE, recursive = TRUE)\n\ndata <- fread(here(commonLoopDir, \\loopScore_Hansen.tsv\\))\ndata <- data %>% \n  filter(\n    !if_all(starts_with(\\ooe\\), ~ .x == 0)   # drop rows where *every* ooe* value is 0\n)\n\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_all_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_str_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_str2_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_reg_dTAGvDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_dTAGvDMSO\\,\n                 title = \\Hansen_ooe_regStr_dTAGvDMSO\\, figDir)\n\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_A485vDMSO\\,\n                 title = \\Hansen_ooe_all_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_A485vDMSO\\,\n                 title = \\Hansen_ooe_str_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_A485vDMSO\\,\n                 title = \\Hansen_ooe_str2_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_A485vDMSO\\,\n                 title = \\Hansen_ooe_reg_A485vDMSO\\, figDir)\nplot_loop_scores(data, ooe_DMSO, ooe_A485, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_A485vDMSO\\,\n                 title = \\Hansen_ooe_regStr_A485vDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_all_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_str_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_str2_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_reg_EpidTAGvEpiDMSO\\, figDir)\nplot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_EpidTAGvEpiDMSO\\,\n                 title = \\Hansen_ooe_regStr_EpidTAGvEpiDMSO\\, figDir)\n\n\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\,\n                                                 \\E-E\\, \\E-S\\, \\E-X\\,\n                                                 \\S-S\\, \\S-X\\,\n                                                 \\X-X\\),\n                 name = \\Hansen_ooe_all_AIDvUT\\,\n                 title = \\Hansen_ooe_all_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\),\n                 name = \\Hansen_ooe_str_AIDvUT\\,\n                 title = \\Hansen_ooe_str_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\S-S\\, \\S-X\\),\n                 name = \\Hansen_ooe_str2_AIDvUT\\,\n                 title = \\Hansen_ooe_str2_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-P\\, \\P-E\\, \\E-E\\),\n                 name = \\Hansen_ooe_reg_AIDvUT\\,\n                 title = \\Hansen_ooe_reg_AIDvUT\\, figDir)\nplot_loop_scores(data, ooe_UT, ooe_AID, c(\\P-S\\, \\E-S\\),\n                 name = \\Hansen_ooe_regStr_AIDvUT\\,\n                 title = \\Hansen_ooe_regStr_AIDvUT\\, figDir)\n\n```\n```"} -->
```r
```r
plot_loop_scores <- function(df, col_x, col_y, annoList,
name, title, figDir,
diffCutoff = 0.5, density_n = 100,
xmin = -0.5, xmax = 1,
ymin = -0.5, ymax = 1) {
col_x_q <- enquo(col_x)
col_y_q <- enquo(col_y)
# Scatterplot
df2 <- df %>%
filter(Anno_simple %in% annoList) %>%
select(id, x = !!col_x_q, y = !!col_y_q) %>%
mutate(
minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),
eps = minpos * 0.1,
lx = log2(x + eps),
ly = log2(y + eps)
)
p1 <- ggplot(df2, aes(x = lx, y = ly)) +
geom_point() +
scale_color_viridis() +
scale_x_continuous(
name = paste0(\log2(\, quo_name(col_x_q), \)\)
) +
scale_y_continuous(
name = paste0(\log2(\, quo_name(col_y_q), \)\)
) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0,
colour = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, colour = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, colour = \grey\) +
geom_abline(slope = 1, intercept = -(2**diffCutoff),
colour = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = (2**diffCutoff),
colour = \grey\, linetype = \dotted\) +
theme_classic() +
labs(x = quo_name(col_x_q),
y = quo_name(col_y_q)) +
ggtitle(title)
fileName <- paste0(\scatterplot_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
png(here(figDir, paste0(fileName, \.png\)),
width = 3.5, height = 3, units = \in\, res = 600)
print(p1)
dev.off()
# Pie chart
df3 <- df %>%
filter(Anno_simple %in% annoList) %>%
select(id, x = !!col_x_q, y = !!col_y_q) %>%
mutate(
minpos = min(c(x, y)[c(x, y) > 0], na.rm = TRUE),
eps = minpos * 0.1,
lx = log2(x + eps),
ly = log2(y + eps)
) %>%
mutate(diff = ly - lx) %>%
mutate(updown = ifelse(diff > diffCutoff, \UP\,
ifelse(diff < -diffCutoff, \DOWN\, \NO\)))
df3$updown <- factor(df3$updown, c(\UP\, \NO\, \DOWN\))
pie_df <- df3 %>%
dplyr::count(updown) %>%
mutate(pct = n / sum(n) * 100,
label = paste0(updown, \\n\, n, \\n(\, round(pct,1), \%)\))
p2 <- ggplot(pie_df, aes(x = \\, y = n, fill = updown)) +
geom_col(color = \white\) +
coord_polar(theta = \y\) +
geom_text(aes(label = label),
position = position_stack(vjust = 0.5),
size = fontSizeM/2) +
theme_void() + theme(legend.position = \none\) +
scale_fill_manual(values = c(strong_red, no_grey, strong_blue))
fileName <- here(figDir, paste0(\piechart_\, name))
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
svglite(paste0(fileName, \.svg\), width = width, height = height)
print(p2)
dev.off()
}
commonLoopDir <- here(\../data/loop_analysis\)
figDir <- here(\../figure/loop_analysis\)
dir.create(figDir, showWarnings = FALSE, recursive = TRUE)
data <- fread(here(commonLoopDir, \loopScore_Hansen.tsv\))
data <- data %>%
filter(
!if_all(starts_with(\ooe\), ~ .x == 0) # drop rows where *every* ooe* value is 0
)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \Hansen_ooe_all_dTAGvDMSO\,
title = \Hansen_ooe_all_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\),
name = \Hansen_ooe_str_dTAGvDMSO\,
title = \Hansen_ooe_str_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\S-S\, \S-X\),
name = \Hansen_ooe_str2_dTAGvDMSO\,
title = \Hansen_ooe_str2_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-P\, \P-E\, \E-E\),
name = \Hansen_ooe_reg_dTAGvDMSO\,
title = \Hansen_ooe_reg_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_dTAG, c(\P-S\, \E-S\),
name = \Hansen_ooe_regStr_dTAGvDMSO\,
title = \Hansen_ooe_regStr_dTAGvDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \Hansen_ooe_all_A485vDMSO\,
title = \Hansen_ooe_all_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\),
name = \Hansen_ooe_str_A485vDMSO\,
title = \Hansen_ooe_str_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\S-S\, \S-X\),
name = \Hansen_ooe_str2_A485vDMSO\,
title = \Hansen_ooe_str2_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-P\, \P-E\, \E-E\),
name = \Hansen_ooe_reg_A485vDMSO\,
title = \Hansen_ooe_reg_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_DMSO, ooe_A485, c(\P-S\, \E-S\),
name = \Hansen_ooe_regStr_A485vDMSO\,
title = \Hansen_ooe_regStr_A485vDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \Hansen_ooe_all_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_all_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\),
name = \Hansen_ooe_str_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_str_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\S-S\, \S-X\),
name = \Hansen_ooe_str2_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_str2_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-P\, \P-E\, \E-E\),
name = \Hansen_ooe_reg_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_reg_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_EpiDMSO, ooe_EpidTAG, c(\P-S\, \E-S\),
name = \Hansen_ooe_regStr_EpidTAGvEpiDMSO\,
title = \Hansen_ooe_regStr_EpidTAGvEpiDMSO\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \P-S\, \P-X\,
\E-E\, \E-S\, \E-X\,
\S-S\, \S-X\,
\X-X\),
name = \Hansen_ooe_all_AIDvUT\,
title = \Hansen_ooe_all_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\),
name = \Hansen_ooe_str_AIDvUT\,
title = \Hansen_ooe_str_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\S-S\, \S-X\),
name = \Hansen_ooe_str2_AIDvUT\,
title = \Hansen_ooe_str2_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-P\, \P-E\, \E-E\),
name = \Hansen_ooe_reg_AIDvUT\,
title = \Hansen_ooe_reg_AIDvUT\, figDir)
plot_loop_scores(data, ooe_UT, ooe_AID, c(\P-S\, \E-S\),
name = \Hansen_ooe_regStr_AIDvUT\,
title = \Hansen_ooe_regStr_AIDvUT\, figDir)
```
```
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\Hansen\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_simple_ensemblList.tsv\\))) %>%\n  filter(\n    !if_all(starts_with(\\ooe\\), ~ .x == 0)   # drop rows where *every* ooe* value is 0\n  ) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_simpleAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_simpleAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \Hansen\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_simple_ensemblList.tsv\))) %>%
filter(
!if_all(starts_with(\ooe\), ~ .x == 0) # drop rows where *every* ooe* value is 0
) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_simpleAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_simpleAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\Hansen\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_simple_ensemblList.tsv\\))) %>%\n  filter(\n    !if_all(starts_with(\\ooe\\), ~ .x == 0)   # drop rows where *every* ooe* value is 0\n  ) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_simpleAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_simpleAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuYWxwaGEgPC0gMC4wNVxuZmNDdXRvZmYgPC0gMC41XG5kaWZmQ3V0b2ZmIDwtIDAuNVxuY29tbW9uTG9vcERpciA8LSBoZXJlKFxcLi4vZGF0YS9sb29wX2FuYWx5c2lzXFwpXG5kaWZmRGlyIDwtIGhlcmUoXFwuLi9kYXRhL1JOQV9kaWZmXFwpXG5uYW1lIDwtIFxcSGFuc2VuXFxcbmdlbmVBbm5vRGF0YSA8LSBsb2FkTG9vcEFubm9EYXRhKGhlcmUoY29tbW9uTG9vcERpciwgcGFzdGUwKFxcbG9vcFNjb3JlX1xcLCBuYW1lLCBcXF9wLW5fc2ltcGxlX2Vuc2VtYmxMaXN0LnRzdlxcKSkpICU+JVxuICBmaWx0ZXIoXG4gICAgIWlmX2FsbChzdGFydHNfd2l0aChcXG9vZVxcKSwgfiAueCA9PSAwKSAgICMgZHJvcCByb3dzIHdoZXJlICpldmVyeSogb29lKiB2YWx1ZSBpcyAwXG4gICkgJT4lXG4gIHJvd3dpc2UoKSAlPiUgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICMgd29yayBvbmUgcm93IGF0IGEgdGltZVxuICBtdXRhdGUoXG4gICAgbWlucG9zID0gbWluKGMob29lX2RUQUcsIG9vZV9ETVNPKVtjKG9vZV9kVEFHLCBvb2VfRE1TTykgPiAwXSwgbmEucm0gPSBUUlVFKSxcbiAgICBlcHMgICAgPSBpZmVsc2UoaXMuZmluaXRlKG1pbnBvcyksIG1pbnBvcyAqIDAuMSwgMC4wMSksICAgICAgIyBmYWxsYmFjayBpZiBib3RoIGFyZSAwXG4gICAgZGlmZiAgID0gbG9nMigob29lX2RUQUcgKyBlcHMpIC8gKG9vZV9ETVNPICsgZXBzKSlcbiAgKSAlPiUgXG4gIHVuZ3JvdXAoKSAlPiUgXG4gIHNlbGVjdCgtbWlucG9zLCAtZXBzKSBcbiAgI2RwbHlyOjptdXRhdGUoZGlmZiA9IGxvZzIob29lX2RUQUcpIC0gbG9nMihvb2VfRE1TTykpXG5cblxuZGlmZi5STkEgPC0gZnJlYWQoaGVyZShkaWZmRGlyLCBcXGRpZmZfRzEuZFRBR19HMS4yaS5kVEFHX3ZzX0cxLjJpLkRNU08udHN2XFwpKSAlPiVcbiAgZHBseXI6OnNlbGVjdChlbnNlbWJsX2dlbmVfaWQsIGxvZzJGb2xkQ2hhbmdlLCBzaHJpbmtlZF9sb2cyRkMsIHBhZGosIGV4dGVybmFsX2dlbmVfbmFtZSlcbmdlbmVMaXN0LmRvd24uUk5BIDwtIChkaWZmLlJOQSAlPiUgZHBseXI6OmZpbHRlcihzaHJpbmtlZF9sb2cyRkMgPD0gMCwgcGFkaiA8IGFscGhhKSkkZW5zZW1ibF9nZW5lX2lkXG5nZW5lTGlzdC51cC5STkEgPC0gKGRpZmYuUk5BICU+JSBkcGx5cjo6ZmlsdGVyKHNocmlua2VkX2xvZzJGQyA+IDAsIHBhZGogPCBhbHBoYSkpJGVuc2VtYmxfZ2VuZV9pZFxuXG5cbm1heExvZzJGQyA8LSAyXG5cbnRlbXAgPC0gZ2VuZUFubm9EYXRhICU+JSBkcGx5cjo6c2VsZWN0KGRpZmYsIGdlbmUpICU+JSBcbiAgdW5uZXN0KGdlbmUpICU+JSBncm91cF9ieShnZW5lKSAlPiVcbiAgc3VtbWFyaXplKG1lYW5fZGlmZl9zY29yZSA9IG1lYW4oZGlmZiksIC5ncm91cHMgPSAnZHJvcCcpXG5cbnRlbXAgPC0gbGVmdF9qb2luKHRlbXAsIGRpZmYuUk5BLCBieSA9IGMoXFxnZW5lXFwgPSBcXGVuc2VtYmxfZ2VuZV9pZFxcKSkgJT4lIFxuICBkcGx5cjo6bXV0YXRlKGZsYWcgPSBpZmVsc2UoZ2VuZSAlaW4lIGdlbmVMaXN0LmRvd24uUk5BLCBcXDJET1dOXFwsXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICBpZmVsc2UoZ2VuZSAlaW4lIGdlbmVMaXN0LnVwLlJOQSwgXFwxVVBcXCwgXFwwTk9cXCkpLFxuICAgICAgICAgICAgICAgIG1heEZsYWcgPSAoYWJzKHNocmlua2VkX2xvZzJGQykgPiBtYXhMb2cyRkMpLFxuICAgICAgICAgICAgICAgIHNocmxvZzJmY01heCA9IHBtYXgocG1pbihzaHJpbmtlZF9sb2cyRkMsIG1heExvZzJGQyksIC1tYXhMb2cyRkMpKSAlPiUgXG4gIGRwbHlyOjphcnJhbmdlKGZsYWcpXG5cbnAgPC0gZ2dwbG90KHRlbXAsIGFlcyh4ID0gc2hybG9nMmZjTWF4LCB5ID0gbWVhbl9kaWZmX3Njb3JlLCBjb2xvciA9IGZsYWcsXG4gICAgICAgICAgICAgICAgICAgICAgbGFiZWwgPSBpZmVsc2UoZmxhZyAhPSBcXDBOT1xcLCBleHRlcm5hbF9nZW5lX25hbWUsIE5BKSxcbiAgICAgICAgICAgICAgICAgICAgICBzaGFwZSA9IGFzLmZhY3RvcihtYXhGbGFnKSkpICtcbiAgZ2VvbV9wb2ludCgpICsgZ2VvbV90ZXh0X3JlcGVsKCkgKyB0aGVtZV9jbGFzc2ljKCkgK1xuICBnZ3RpdGxlKHBhc3RlMChuYW1lLCBcXF9vb2Vfc2ltcGxlQW5ub1xcKSkgK1xuICBnZW9tX2hsaW5lKHlpbnRlcmNlcHQgPSAwKSArXG4gIGdlb21faGxpbmUoeWludGVyY2VwdCA9IGRpZmZDdXRvZmYsIGxpbmV0eXBlID0gXFxkYXNoZWRcXCkgK1xuICBnZW9tX2hsaW5lKHlpbnRlcmNlcHQgPSAtIGRpZmZDdXRvZmYsIGxpbmV0eXBlID0gXFxkYXNoZWRcXCkgK1xuICBnZW9tX3ZsaW5lKHhpbnRlcmNlcHQgPSAwKSArXG4gIGdlb21fdmxpbmUoeGludGVyY2VwdCA9IGZjQ3V0b2ZmLCBsaW5ldHlwZSA9IFxcZGFzaGVkXFwpICtcbiAgZ2VvbV92bGluZSh4aW50ZXJjZXB0ID0gLWZjQ3V0b2ZmLCBsaW5ldHlwZSA9IFxcZGFzaGVkXFwpICtcbiAgc2NhbGVfY29sb3JfbWFudWFsKHZhbHVlcyA9IGMoXFwwTk9cXCA9IFxcZ3JleVxcLCBcXDFVUFxcID0gXFxyZWRcXCwgXFwyRE9XTlxcID0gXFxibHVlXFwpKSArICAjIENvcnJlY3RlZCBjb2xvciBtYXBwaW5nXG4gIHNjYWxlX3NoYXBlX21hbnVhbCh2YWx1ZXMgPSBjKFxcVFJVRVxcID0gMiwgXFxGQUxTRVxcID0gMTkpKVxuXG5maWxlTmFtZSA8LSBwYXN0ZTAoXFxSTkF2c0xvb3BfXFwsIG5hbWUsIFxcX29vZV9zaW1wbGVBbm5vXFwpXG5oZWlnaHQgPC0gNFxud2lkdGggPC0gN1xucG5nKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnBuZ1xcKSksIHJlcyA9IDYwMCwgdW5pdCA9IFxcaW5cXCwgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuc3ZnbGl0ZShoZXJlKGZpZ0RpciwgcGFzdGUwKGZpbGVOYW1lLCBcXC5zdmdcXCkpLCAgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuYGBgXG5gYGAifQ== -->
```r
```r
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \Hansen\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_simple_ensemblList.tsv\))) %>%
filter(
!if_all(starts_with(\ooe\), ~ .x == 0) # drop rows where *every* ooe* value is 0
) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_simpleAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_simpleAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Scatterplot - Hansen
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\Hansen\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_complex_ensemblList.tsv\\))) %>%\n  filter(\n    !if_all(starts_with(\\ooe\\), ~ .x == 0)   # drop rows where *every* ooe* value is 0\n  ) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_complexAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_complexAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \Hansen\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_complex_ensemblList.tsv\))) %>%
filter(
!if_all(starts_with(\ooe\), ~ .x == 0) # drop rows where *every* ooe* value is 0
) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_complexAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_complexAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuYWxwaGEgPC0gMC4wNVxuZmNDdXRvZmYgPC0gMC41XG5kaWZmQ3V0b2ZmIDwtIDAuNVxuY29tbW9uTG9vcERpciA8LSBoZXJlKFxcLi4vZGF0YS9sb29wX2FuYWx5c2lzXFwpXG5kaWZmRGlyIDwtIGhlcmUoXFwuLi9kYXRhL1JOQV9kaWZmXFwpXG5uYW1lIDwtIFxcSGFuc2VuXFxcbmdlbmVBbm5vRGF0YSA8LSBsb2FkTG9vcEFubm9EYXRhKGhlcmUoY29tbW9uTG9vcERpciwgcGFzdGUwKFxcbG9vcFNjb3JlX1xcLCBuYW1lLCBcXF9wLW5fY29tcGxleF9lbnNlbWJsTGlzdC50c3ZcXCkpKSAlPiVcbiAgZmlsdGVyKFxuICAgICFpZl9hbGwoc3RhcnRzX3dpdGgoXFxvb2VcXCksIH4gLnggPT0gMCkgICAjIGRyb3Agcm93cyB3aGVyZSAqZXZlcnkqIG9vZSogdmFsdWUgaXMgMFxuICApICU+JVxuICByb3d3aXNlKCkgJT4lICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAjIHdvcmsgb25lIHJvdyBhdCBhIHRpbWVcbiAgbXV0YXRlKFxuICAgIG1pbnBvcyA9IG1pbihjKG9vZV9kVEFHLCBvb2VfRE1TTylbYyhvb2VfZFRBRywgb29lX0RNU08pID4gMF0sIG5hLnJtID0gVFJVRSksXG4gICAgZXBzICAgID0gaWZlbHNlKGlzLmZpbml0ZShtaW5wb3MpLCBtaW5wb3MgKiAwLjEsIDAuMDEpLCAgICAgICMgZmFsbGJhY2sgaWYgYm90aCBhcmUgMFxuICAgIGRpZmYgICA9IGxvZzIoKG9vZV9kVEFHICsgZXBzKSAvIChvb2VfRE1TTyArIGVwcykpXG4gICkgJT4lIFxuICB1bmdyb3VwKCkgJT4lIFxuICBzZWxlY3QoLW1pbnBvcywgLWVwcykgXG4gICNkcGx5cjo6bXV0YXRlKGRpZmYgPSBsb2cyKG9vZV9kVEFHKSAtIGxvZzIob29lX0RNU08pKVxuXG5kaWZmLlJOQSA8LSBmcmVhZChoZXJlKGRpZmZEaXIsIFxcZGlmZl9HMS5kVEFHX0cxLjJpLmRUQUdfdnNfRzEuMmkuRE1TTy50c3ZcXCkpICU+JVxuICBkcGx5cjo6c2VsZWN0KGVuc2VtYmxfZ2VuZV9pZCwgbG9nMkZvbGRDaGFuZ2UsIHNocmlua2VkX2xvZzJGQywgcGFkaiwgZXh0ZXJuYWxfZ2VuZV9uYW1lKVxuZ2VuZUxpc3QuZG93bi5STkEgPC0gKGRpZmYuUk5BICU+JSBkcGx5cjo6ZmlsdGVyKHNocmlua2VkX2xvZzJGQyA8PSAwLCBwYWRqIDwgYWxwaGEpKSRlbnNlbWJsX2dlbmVfaWRcbmdlbmVMaXN0LnVwLlJOQSA8LSAoZGlmZi5STkEgJT4lIGRwbHlyOjpmaWx0ZXIoc2hyaW5rZWRfbG9nMkZDID4gMCwgcGFkaiA8IGFscGhhKSkkZW5zZW1ibF9nZW5lX2lkXG5cblxubWF4TG9nMkZDIDwtIDJcblxudGVtcCA8LSBnZW5lQW5ub0RhdGEgJT4lIGRwbHlyOjpzZWxlY3QoZGlmZiwgZ2VuZSkgJT4lIFxuICB1bm5lc3QoZ2VuZSkgJT4lIGdyb3VwX2J5KGdlbmUpICU+JVxuICBzdW1tYXJpemUobWVhbl9kaWZmX3Njb3JlID0gbWVhbihkaWZmKSwgLmdyb3VwcyA9ICdkcm9wJylcblxudGVtcCA8LSBsZWZ0X2pvaW4odGVtcCwgZGlmZi5STkEsIGJ5ID0gYyhcXGdlbmVcXCA9IFxcZW5zZW1ibF9nZW5lX2lkXFwpKSAlPiUgXG4gIGRwbHlyOjptdXRhdGUoZmxhZyA9IGlmZWxzZShnZW5lICVpbiUgZ2VuZUxpc3QuZG93bi5STkEsIFxcMkRPV05cXCxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGlmZWxzZShnZW5lICVpbiUgZ2VuZUxpc3QudXAuUk5BLCBcXDFVUFxcLCBcXDBOT1xcKSksXG4gICAgICAgICAgICAgICAgbWF4RmxhZyA9IChhYnMoc2hyaW5rZWRfbG9nMkZDKSA+IG1heExvZzJGQyksXG4gICAgICAgICAgICAgICAgc2hybG9nMmZjTWF4ID0gcG1heChwbWluKHNocmlua2VkX2xvZzJGQywgbWF4TG9nMkZDKSwgLW1heExvZzJGQykpICU+JSBcbiAgZHBseXI6OmFycmFuZ2UoZmxhZylcblxucCA8LSBnZ3Bsb3QodGVtcCwgYWVzKHggPSBzaHJsb2cyZmNNYXgsIHkgPSBtZWFuX2RpZmZfc2NvcmUsIGNvbG9yID0gZmxhZyxcbiAgICAgICAgICAgICAgICAgICAgICBsYWJlbCA9IGlmZWxzZShmbGFnICE9IFxcME5PXFwsIGV4dGVybmFsX2dlbmVfbmFtZSwgTkEpLFxuICAgICAgICAgICAgICAgICAgICAgIHNoYXBlID0gYXMuZmFjdG9yKG1heEZsYWcpKSkgK1xuICBnZW9tX3BvaW50KCkgKyBnZW9tX3RleHRfcmVwZWwoKSArIHRoZW1lX2NsYXNzaWMoKSArXG4gIGdndGl0bGUocGFzdGUwKG5hbWUsIFxcX29vZV9jb21wbGV4QW5ub1xcKSkgK1xuICBnZW9tX2hsaW5lKHlpbnRlcmNlcHQgPSAwKSArXG4gIGdlb21faGxpbmUoeWludGVyY2VwdCA9IGRpZmZDdXRvZmYsIGxpbmV0eXBlID0gXFxkYXNoZWRcXCkgK1xuICBnZW9tX2hsaW5lKHlpbnRlcmNlcHQgPSAtIGRpZmZDdXRvZmYsIGxpbmV0eXBlID0gXFxkYXNoZWRcXCkgK1xuICBnZW9tX3ZsaW5lKHhpbnRlcmNlcHQgPSAwKSArXG4gIGdlb21fdmxpbmUoeGludGVyY2VwdCA9IGZjQ3V0b2ZmLCBsaW5ldHlwZSA9IFxcZGFzaGVkXFwpICtcbiAgZ2VvbV92bGluZSh4aW50ZXJjZXB0ID0gLWZjQ3V0b2ZmLCBsaW5ldHlwZSA9IFxcZGFzaGVkXFwpICtcbiAgc2NhbGVfY29sb3JfbWFudWFsKHZhbHVlcyA9IGMoXFwwTk9cXCA9IFxcZ3JleVxcLCBcXDFVUFxcID0gXFxyZWRcXCwgXFwyRE9XTlxcID0gXFxibHVlXFwpKSArICAjIENvcnJlY3RlZCBjb2xvciBtYXBwaW5nXG4gIHNjYWxlX3NoYXBlX21hbnVhbCh2YWx1ZXMgPSBjKFxcVFJVRVxcID0gMiwgXFxGQUxTRVxcID0gMTkpKVxuXG5maWxlTmFtZSA8LSBwYXN0ZTAoXFxSTkF2c0xvb3BfXFwsIG5hbWUsIFxcX29vZV9jb21wbGV4QW5ub1xcKVxuaGVpZ2h0IDwtIDRcbndpZHRoIDwtIDdcbnBuZyhoZXJlKGZpZ0RpciwgcGFzdGUwKGZpbGVOYW1lLCBcXC5wbmdcXCkpLCByZXMgPSA2MDAsIHVuaXQgPSBcXGluXFwsIGhlaWdodCA9IGhlaWdodCwgd2lkdGggPSB3aWR0aClcbnByaW50KHApXG5kZXYub2ZmKClcbnN2Z2xpdGUoaGVyZShmaWdEaXIsIHBhc3RlMChmaWxlTmFtZSwgXFwuc3ZnXFwpKSwgIGhlaWdodCA9IGhlaWdodCwgd2lkdGggPSB3aWR0aClcbnByaW50KHApXG5kZXYub2ZmKClcbmBgYFxuYGBgIn0= -->\n\n```r\n```r\nalpha <- 0.05\nfcCutoff <- 0.5\ndiffCutoff <- 0.5\ncommonLoopDir <- here(\\../data/loop_analysis\\)\ndiffDir <- here(\\../data/RNA_diff\\)\nname <- \\Hansen\\\ngeneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\\loopScore_\\, name, \\_p-n_complex_ensemblList.tsv\\))) %>%\n  filter(\n    !if_all(starts_with(\\ooe\\), ~ .x == 0)   # drop rows where *every* ooe* value is 0\n  ) %>%\n  rowwise() %>%                                # work one row at a time\n  mutate(\n    minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),\n    eps    = ifelse(is.finite(minpos), minpos * 0.1, 0.01),      # fallback if both are 0\n    diff   = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))\n  ) %>% \n  ungroup() %>% \n  select(-minpos, -eps) \n  #dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))\n\ndiff.RNA <- fread(here(diffDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id\n\n\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() +\n  ggtitle(paste0(name, \\_ooe_complexAnno\\)) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\RNAvsLoop_\\, name, \\_ooe_complexAnno\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
alpha <- 0.05
fcCutoff <- 0.5
diffCutoff <- 0.5
commonLoopDir <- here(\../data/loop_analysis\)
diffDir <- here(\../data/RNA_diff\)
name <- \Hansen\
geneAnnoData <- loadLoopAnnoData(here(commonLoopDir, paste0(\loopScore_\, name, \_p-n_complex_ensemblList.tsv\))) %>%
filter(
!if_all(starts_with(\ooe\), ~ .x == 0) # drop rows where *every* ooe* value is 0
) %>%
rowwise() %>% # work one row at a time
mutate(
minpos = min(c(ooe_dTAG, ooe_DMSO)[c(ooe_dTAG, ooe_DMSO) > 0], na.rm = TRUE),
eps = ifelse(is.finite(minpos), minpos * 0.1, 0.01), # fallback if both are 0
diff = log2((ooe_dTAG + eps) / (ooe_DMSO + eps))
) %>%
ungroup() %>%
select(-minpos, -eps)
#dplyr::mutate(diff = log2(ooe_dTAG) - log2(ooe_DMSO))
diff.RNA <- fread(here(diffDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC <= 0, padj < alpha))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA %>% dplyr::filter(shrinked_log2FC > 0, padj < alpha))$ensembl_gene_id
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(paste0(name, \_ooe_complexAnno\)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\RNAvsLoop_\, name, \_ooe_complexAnno\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### RNA-seq loopscore scatterplot
<!-- rnb-text-end -->
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commonLoopDir <- here(\../data/loop_analysis\)
diffCutoff <- 0.2
data <- fread(here(commonLoopDir, \loopScore_chromosight.tsv\))
score.tb <- data %>% dplyr::filter(Anno_simple %in% c(\S-S\)) %>%
dplyr::select(id, score_DMSO, score_dTAG)
### P1. UT vs AID
score.tb$density <- get_density(score.tb$score_DMSO, score.tb$score_dTAG, n = 200)
score.tb <- score.tb %>% dplyr::arrange(density)
p1 <- ggplot(score.tb, aes(x = score_DMSO, y = score_dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\))
# Define the data
categories <- c(\Up\, \No\, \Down\)
values <- c(228, 9084, 57169)
# Calculate percentages
percentages <- round((values / sum(values)) * 100, 2)
# Combine values and percentages for labels
labels <- paste(categories, \\n\, values, \\n(\, percentages, \%)\, sep = \\)
fileName <- here(figDir, \piechart_unionLoops\)
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
svglite(paste0(fileName, \.svg\), width = width, height =height)
pie(values,
labels = labels,
col = c(strong_red, no_grey, strong_blue),
cex = fontSizeM/12)
dev.off()
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```r
```r
commonLoopDir <- here(\../data/loop_analysis\)
diffCutoff <- 0.2
data <- fread(here(commonLoopDir, \loopScore_chromosight.tsv\))
score.tb <- data %>% dplyr::filter(Anno_simple %in% c(\S-S\)) %>%
dplyr::select(id, score_DMSO, score_dTAG)
### P1. UT vs AID
score.tb$density <- get_density(score.tb$score_DMSO, score.tb$score_dTAG, n = 200)
score.tb <- score.tb %>% dplyr::arrange(density)
p1 <- ggplot(score.tb, aes(x = score_DMSO, y = score_dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\))
# Define the data
categories <- c(\Up\, \No\, \Down\)
values <- c(228, 9084, 57169)
# Calculate percentages
percentages <- round((values / sum(values)) * 100, 2)
# Combine values and percentages for labels
labels <- paste(categories, \\n\, values, \\n(\, percentages, \%)\, sep = \\)
fileName <- here(figDir, \piechart_unionLoops\)
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
svglite(paste0(fileName, \.svg\), width = width, height =height)
pie(values,
labels = labels,
col = c(strong_red, no_grey, strong_blue),
cex = fontSizeM/12)
dev.off()
```
```
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<!-- rnb-output-begin 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 -->
commonLoopDir <- here(\../data/loop_analysis\)
diffCutoff <- 0.2
data <- fread(here(commonLoopDir, \loopScore_chromosight.tsv\))
score.tb <- data %>% dplyr::filter(Anno_simple %in% c(\P-P\, \P-E\, \E-E\)) %>%
dplyr::select(id, ooe_DMSO, ooe_dTAG) %>%
dplyr::filter(!is.na(ooe_DMSO), !is.na(ooe_dTAG))
### P1. UT vs AID
score.tb$density <- get_density(score.tb$ooe_DMSO, score.tb$ooe_dTAG, n = 200)
score.tb <- score.tb %>% dplyr::arrange(density)
p1 <- ggplot(score.tb, aes(x = ooe_DMSO, y = ooe_dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
coord_fixed() +
xlim(-0.5, 5) + ylim(-0.5, 5) +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\))
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY29tbW9uTG9vcERpciA8LSBoZXJlKFxcLi4vZGF0YS9sb29wX2FuYWx5c2lzXFwpXG5cbmRpZmZDdXRvZmYgPC0gMC4yXG5cbmRhdGEgPC0gZnJlYWQoaGVyZShjb21tb25Mb29wRGlyLCBcXGxvb3BTY29yZV9jaHJvbW9zaWdodC50c3ZcXCkpXG5cbnNjb3JlLnRiIDwtIGRhdGEgJT4lIGRwbHlyOjpmaWx0ZXIoQW5ub19zaW1wbGUgJWluJSBjKFxcUC1QXFwsIFxcUC1FXFwsIFxcRS1FXFwpKSAlPiVcbiAgZHBseXI6OnNlbGVjdChpZCwgb29lX0RNU08sIG9vZV9kVEFHKSAlPiVcbiAgZHBseXI6OmZpbHRlcighaXMubmEob29lX0RNU08pLCAhaXMubmEob29lX2RUQUcpKVxuXG5cbiMjIyBQMS4gVVQgdnMgQUlEXG5zY29yZS50YiRkZW5zaXR5IDwtIGdldF9kZW5zaXR5KHNjb3JlLnRiJG9vZV9ETVNPLCBzY29yZS50YiRvb2VfZFRBRywgbiA9IDIwMClcbnNjb3JlLnRiIDwtIHNjb3JlLnRiICU+JSBkcGx5cjo6YXJyYW5nZShkZW5zaXR5KVxucDEgPC0gZ2dwbG90KHNjb3JlLnRiLCBhZXMoeCA9IG9vZV9ETVNPLCB5ID0gb29lX2RUQUcsIGNvbG9yID0gZGVuc2l0eSkpICtcbiAgZ2VvbV9wb2ludCgpICsgXG4gIHNjYWxlX2NvbG9yX3ZpcmlkaXMoKSArXG4gIGNvb3JkX2ZpeGVkKCkgK1xuICAgIHhsaW0oLTAuNSwgNSkgKyB5bGltKC0wLjUsIDUpICtcblxuICBnZW9tX2FibGluZShzbG9wZSA9IDEsIGludGVyY2VwdCA9IDAsIGNvbCA9IFxcZ3JleTUwXFwsIGxpbmV0eXBlID0gXFxkYXNoZWRcXCkgK1xuICBnZW9tX2hsaW5lKHlpbnRlcmNlcHQgPSAwLCBhbHBoYSA9IDAuNSwgY29sb3IgPSBcXGdyZXlcXCkgK1xuICBnZW9tX3ZsaW5lKHhpbnRlcmNlcHQgPSAwLCBhbHBoYSA9IDAuNSwgY29sb3IgPSBcXGdyZXlcXCkgK1xuICBnZW9tX2FibGluZShzbG9wZSA9IDEsIGludGVyY2VwdCA9IC1kaWZmQ3V0b2ZmLCBjb2wgPSBcXGdyZXlcXCwgbGluZXR5cGUgPSBcXGRvdHRlZFxcKSArXG4gIGdlb21fYWJsaW5lKHNsb3BlID0gMSwgaW50ZXJjZXB0ID0gZGlmZkN1dG9mZiwgY29sID0gXFxncmV5XFwsIGxpbmV0eXBlID0gXFxkb3R0ZWRcXCkgK1xuICB0aGVtZV9jbGFzc2ljKCkgKyBnZ3RpdGxlKHBhc3RlMChcXENvbnNlbnN1cyBsb29wIHNjb3JlXFwpKVxuYGBgXG5gYGAifQ== -->
```r
```r
commonLoopDir <- here(\../data/loop_analysis\)
diffCutoff <- 0.2
data <- fread(here(commonLoopDir, \loopScore_chromosight.tsv\))
score.tb <- data %>% dplyr::filter(Anno_simple %in% c(\P-P\, \P-E\, \E-E\)) %>%
dplyr::select(id, ooe_DMSO, ooe_dTAG) %>%
dplyr::filter(!is.na(ooe_DMSO), !is.na(ooe_dTAG))
### P1. UT vs AID
score.tb$density <- get_density(score.tb$ooe_DMSO, score.tb$ooe_dTAG, n = 200)
score.tb <- score.tb %>% dplyr::arrange(density)
p1 <- ggplot(score.tb, aes(x = ooe_DMSO, y = ooe_dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
coord_fixed() +
xlim(-0.5, 5) + ylim(-0.5, 5) +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\))
```
```
<!-- rnb-source-end -->
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# ARCHIVE
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library(dplyr)
library(ggplot2)
library(viridis)
library(purrr)
# your density fn
get_density <- function(x, y, n = 100) {
# … whatever you already have …
}
# 1) make a general plotting function
plot_compare <- function(df, x_col, y_col, diffCutoff = 0.1, lim = c(-0.5, 1)) {
df2 <- df %>%
select(all_of(c(x_col, y_col))) %>%
rename(x = !!sym(x_col), y = !!sym(y_col)) %>%
mutate(density = get_density(x, y, n = 100)) %>%
arrange(density)
ggplot(df2, aes(x = x, y = y, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(lim) + ylim(lim) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, color = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, color = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, color = \grey\, linetype = \dotted\) +
theme_classic() +
ggtitle(paste(x_col, \vs\, y_col))
}
# 2) define the pairs you care about
pairs <- list(
DMSO_vs_dTAG = c(\score_DMSO\, \score_dTAG\),
UT_vs_AID = c(\score_UT\, \score_AID\)
# … add more …
)
# 3) build a named list of ggplots
plots <- map(pairs, ~ plot_compare(data, .x[1], .x[2], diffCutoff = diffCutoff))
# now you can print them all:
walk(plots, print)
# or arrange them with patchwork / gridExtra, etc.
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```r
```r
library(dplyr)
library(ggplot2)
library(viridis)
library(purrr)
# your density fn
get_density <- function(x, y, n = 100) {
# … whatever you already have …
}
# 1) make a general plotting function
plot_compare <- function(df, x_col, y_col, diffCutoff = 0.1, lim = c(-0.5, 1)) {
df2 <- df %>%
select(all_of(c(x_col, y_col))) %>%
rename(x = !!sym(x_col), y = !!sym(y_col)) %>%
mutate(density = get_density(x, y, n = 100)) %>%
arrange(density)
ggplot(df2, aes(x = x, y = y, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(lim) + ylim(lim) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, color = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, color = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, color = \grey\, linetype = \dotted\) +
theme_classic() +
ggtitle(paste(x_col, \vs\, y_col))
}
# 2) define the pairs you care about
pairs <- list(
DMSO_vs_dTAG = c(\score_DMSO\, \score_dTAG\),
UT_vs_AID = c(\score_UT\, \score_AID\)
# … add more …
)
# 3) build a named list of ggplots
plots <- map(pairs, ~ plot_compare(data, .x[1], .x[2], diffCutoff = diffCutoff))
# now you can print them all:
walk(plots, print)
# or arrange them with patchwork / gridExtra, etc.
```
```
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nsample <- \\G1.DMSO.Merged\\\nfor(sample in c(\\G1.DMSO.Merged\\, \\G1.dTAG.Merged\\, \\G1.A485.Merged\\)){\n  temp.25kb <- fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_25kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.25kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp.10kb <- fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_10kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.10kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp.5kb <-fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_5kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.5kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)\n  fwrite(temp, here(consensusDir, paste0(\\loopScore_\\, sample, \\.tsv\\)), sep = \\\\t\\)\n}\n\n\ntemp.DMSO <- fread(here(consensusDir, \\loopScore_G1.DMSO.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.DMSO) <- c(\\id\\, \\obs_DMSO\\, \\obsexp_DMSO\\)\ntemp.dTAG <- fread(here(consensusDir, \\loopScore_G1.dTAG.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.dTAG) <- c(\\id\\, \\obs_dTAG\\, \\obsexp_dTAG\\)\ntemp.A485 <- fread(here(consensusDir, \\loopScore_G1.A485.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.A485) <- c(\\id\\, \\obs_A485\\, \\obsexp_A485\\)\n\nloops_oe <- full_join(full_join(temp.DMSO, temp.dTAG, by = \\id\\),\n          temp.A485, by = \\id\\) %>%\n  dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),\n                oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))\n\nfwrite(loops_oe, here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\)), sep = \\\\t\\)\n```\n\n<!-- rnb-source-end -->\n"} -->
sample <- \G1.DMSO.Merged\
for(sample in c(\G1.DMSO.Merged\, \G1.dTAG.Merged\, \G1.A485.Merged\)){
temp.25kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_25kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.25kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.10kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_10kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.10kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.5kb <-fread(here(consensusDir, paste0(\loopScore_\, sample, \_5kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.5kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)
fwrite(temp, here(consensusDir, paste0(\loopScore_\, sample, \.tsv\)), sep = \\t\)
}
temp.DMSO <- fread(here(consensusDir, \loopScore_G1.DMSO.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.DMSO) <- c(\id\, \obs_DMSO\, \obsexp_DMSO\)
temp.dTAG <- fread(here(consensusDir, \loopScore_G1.dTAG.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.dTAG) <- c(\id\, \obs_dTAG\, \obsexp_dTAG\)
temp.A485 <- fread(here(consensusDir, \loopScore_G1.A485.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.A485) <- c(\id\, \obs_A485\, \obsexp_A485\)
loops_oe <- full_join(full_join(temp.DMSO, temp.dTAG, by = \id\),
temp.A485, by = \id\) %>%
dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),
oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))
fwrite(loops_oe, here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\)), sep = \\t\)
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start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.25kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp.10kb <- fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_10kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.10kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp.5kb <-fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_5kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.5kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)\n  fwrite(temp, here(consensusDir, paste0(\\loopScore_\\, sample, \\.tsv\\)), sep = \\\\t\\)\n}\n\n\ntemp.DMSO <- fread(here(consensusDir, \\loopScore_G1.DMSO.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.DMSO) <- c(\\id\\, \\obs_DMSO\\, \\obsexp_DMSO\\)\ntemp.dTAG <- fread(here(consensusDir, \\loopScore_G1.dTAG.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.dTAG) <- c(\\id\\, \\obs_dTAG\\, \\obsexp_dTAG\\)\ntemp.A485 <- fread(here(consensusDir, \\loopScore_G1.A485.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.A485) <- c(\\id\\, \\obs_A485\\, \\obsexp_A485\\)\n\nloops_oe <- full_join(full_join(temp.DMSO, temp.dTAG, by = \\id\\),\n          temp.A485, by = \\id\\) %>%\n  dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),\n                oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))\n\nfwrite(loops_oe, here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\)), sep = \\\\t\\)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
sample <- \G1.DMSO.Merged\
for(sample in c(\G1.DMSO.Merged\, \G1.dTAG.Merged\, \G1.A485.Merged\)){
temp.25kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_25kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.25kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.10kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_10kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.10kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.5kb <-fread(here(consensusDir, paste0(\loopScore_\, sample, \_5kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.5kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)
fwrite(temp, here(consensusDir, paste0(\loopScore_\, sample, \.tsv\)), sep = \\t\)
}
temp.DMSO <- fread(here(consensusDir, \loopScore_G1.DMSO.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.DMSO) <- c(\id\, \obs_DMSO\, \obsexp_DMSO\)
temp.dTAG <- fread(here(consensusDir, \loopScore_G1.dTAG.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.dTAG) <- c(\id\, \obs_dTAG\, \obsexp_dTAG\)
temp.A485 <- fread(here(consensusDir, \loopScore_G1.A485.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.A485) <- c(\id\, \obs_A485\, \obsexp_A485\)
loops_oe <- full_join(full_join(temp.DMSO, temp.dTAG, by = \id\),
temp.A485, by = \id\) %>%
dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),
oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))
fwrite(loops_oe, here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\)), sep = \\t\)
```
```
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-->\n\n```r\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
diffCutoff = 0.2
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- fread(here(consensusDir, \chromo_cons_score_async.tsv\)) %>%
dplyr::select(id, UT, AID)
score.tb <- data %>% dplyr::full_join(temp, by = c(\id\)) %>%
dplyr::mutate(diff_G1 = dTAG - DMSO,
diff_async = AID - UT)
### P1. UT vs AID
score.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$UT, score.tb$AID)
p1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P1. DMSO vs dTAG
score.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$dTAG)
p2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs A485
score.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$A485)
p3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs UT
score.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$UT)
p4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. dTAG vs AID
score.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$dTAG, score.tb$AID)
p5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. diff
score.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$diff_G1, score.tb$diff_async)
p6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-1, 0.5) + ylim(-1, 0.5) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -1, y = 0.5, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
png(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync.png\)), res = 600, units = \in\, width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
svglite(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync.svg\)), width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n```\n```"} -->
```r
```r
diffCutoff = 0.2
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- fread(here(consensusDir, \chromo_cons_score_async.tsv\)) %>%
dplyr::select(id, UT, AID)
score.tb <- data %>% dplyr::full_join(temp, by = c(\id\)) %>%
dplyr::mutate(diff_G1 = dTAG - DMSO,
diff_async = AID - UT)
### P1. UT vs AID
score.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$UT, score.tb$AID)
p1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P1. DMSO vs dTAG
score.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$dTAG)
p2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs A485
score.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$A485)
p3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs UT
score.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$UT)
p4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. dTAG vs AID
score.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$dTAG, score.tb$AID)
p5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. diff
score.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$diff_G1, score.tb$diff_async)
p6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-1, 0.5) + ylim(-1, 0.5) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -1, y = 0.5, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
png(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync.png\)), res = 600, units = \in\, width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
svglite(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync.svg\)), width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
```
```
<!-- rnb-source-end -->
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<!-- rnb-text-begin -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT) %>%\n  dplyr::filter(id %in% regID)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n    theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n########## Saving Async and G1 specific regulatory loop\ndiffCutoff <- 0.2\ntemp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async >= -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async < -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async >= -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async < -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n```\n\n<!-- rnb-source-end -->\n"} -->
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\)) %>%
dplyr::mutate(res = V3 - V2,
id = paste(V1, res, V2, V5, sep = \_\))
regID <- temp$id
diffCutoff = 0.2
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- fread(here(consensusDir, \chromo_cons_score_async.tsv\)) %>%
dplyr::select(id, UT, AID)
score.tb <- data %>% dplyr::full_join(temp, by = c(\id\)) %>%
dplyr::mutate(diff_G1 = dTAG - DMSO,
diff_async = AID - UT) %>%
dplyr::filter(id %in% regID)
### P1. UT vs AID
score.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$UT, score.tb$AID)
p1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P1. DMSO vs dTAG
score.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$dTAG)
p2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs A485
score.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$A485)
p3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs UT
score.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$UT)
p4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. dTAG vs AID
score.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$dTAG, score.tb$AID)
p5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. diff
score.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$diff_G1, score.tb$diff_async)
p6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-1, 0.5) + ylim(-1, 0.5) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -1, y = 0.5, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
png(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.png\)), res = 600, units = \in\, width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
svglite(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.svg\)), width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
########## Saving Async and G1 specific regulatory loop
diffCutoff <- 0.2
temp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async >= -diffCutoff) %>%
dplyr::select(seq(1, 7))
fwrite(temp, here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\), sep = \\t\, col.names = FALSE)
temp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async < -diffCutoff) %>%
dplyr::select(seq(1, 7))
fwrite(temp, here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\), sep = \\t\, col.names = FALSE)
temp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async >= -diffCutoff) %>%
dplyr::select(seq(1, 7))
fwrite(temp, here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\), sep = \\t\, col.names = FALSE)
temp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async < -diffCutoff) %>%
dplyr::select(seq(1, 7))
fwrite(temp, here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\), sep = \\t\, col.names = FALSE)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT) %>%\n  dplyr::filter(id %in% regID)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n    theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n########## Saving Async and G1 specific regulatory loop\ndiffCutoff <- 0.2\ntemp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async >= -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async < -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async >= -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async < -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT) %>%\n  dplyr::filter(id %in% regID)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n    theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n########## Saving Async and G1 specific regulatory loop\ndiffCutoff <- 0.2\ntemp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async >= -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async < -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async >= -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async < -diffCutoff) %>%\n  dplyr::select(seq(1, 7))\nfwrite(temp, here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n```\n```"} -->
```r
```r
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\)) %>%
dplyr::mutate(res = V3 - V2,
id = paste(V1, res, V2, V5, sep = \_\))
regID <- temp$id
diffCutoff = 0.2
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- fread(here(consensusDir, \chromo_cons_score_async.tsv\)) %>%
dplyr::select(id, UT, AID)
score.tb <- data %>% dplyr::full_join(temp, by = c(\id\)) %>%
dplyr::mutate(diff_G1 = dTAG - DMSO,
diff_async = AID - UT) %>%
dplyr::filter(id %in% regID)
### P1. UT vs AID
score.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$UT, score.tb$AID)
p1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P1. DMSO vs dTAG
score.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$dTAG)
p2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs A485
score.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$A485)
p3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs UT
score.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$UT)
p4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. dTAG vs AID
score.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$dTAG, score.tb$AID)
p5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. diff
score.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$diff_G1, score.tb$diff_async)
p6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-1, 0.5) + ylim(-1, 0.5) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -1, y = 0.5, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
png(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.png\)), res = 600, units = \in\, width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
svglite(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync_reg.svg\)), width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
########## Saving Async and G1 specific regulatory loop
diffCutoff <- 0.2
temp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async >= -diffCutoff) %>%
dplyr::select(seq(1, 7))
fwrite(temp, here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\), sep = \\t\, col.names = FALSE)
temp <- score.tb %>% dplyr::filter(diff_G1 >= -diffCutoff, diff_async < -diffCutoff) %>%
dplyr::select(seq(1, 7))
fwrite(temp, here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\), sep = \\t\, col.names = FALSE)
temp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async >= -diffCutoff) %>%
dplyr::select(seq(1, 7))
fwrite(temp, here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\), sep = \\t\, col.names = FALSE)
temp <- score.tb %>% dplyr::filter(diff_G1 < -diffCutoff, diff_async < -diffCutoff) %>%
dplyr::select(seq(1, 7))
fwrite(temp, here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\), sep = \\t\, col.names = FALSE)
```
```
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<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
#### Comparing G1 vs Async for consensus loops
##### All loops
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT) %>%\n  dplyr::filter(id %in% regID)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure.bedpe\)) %>%
dplyr::mutate(res = V3 - V2,
id = paste(V1, res, V2, V5, sep = \_\))
regID <- temp$id
diffCutoff = 0.2
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- fread(here(consensusDir, \chromo_cons_score_async.tsv\)) %>%
dplyr::select(id, UT, AID)
score.tb <- data %>% dplyr::full_join(temp, by = c(\id\)) %>%
dplyr::mutate(diff_G1 = dTAG - DMSO,
diff_async = AID - UT) %>%
dplyr::filter(id %in% regID)
### P1. UT vs AID
score.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$UT, score.tb$AID)
p1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P1. DMSO vs dTAG
score.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$dTAG)
p2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs A485
score.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$A485)
p3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs UT
score.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$UT)
p4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. dTAG vs AID
score.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$dTAG, score.tb$AID)
p5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. diff
score.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$diff_G1, score.tb$diff_async)
p6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-1, 0.5) + ylim(-1, 0.5) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -1, y = 0.5, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
png(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.png\)), res = 600, units = \in\, width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
svglite(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.svg\)), width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT) %>%\n  dplyr::filter(id %in% regID)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ndiffCutoff = 0.2\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\ntemp <- fread(here(consensusDir, \\chromo_cons_score_async.tsv\\)) %>%\n  dplyr::select(id, UT, AID)\n\nscore.tb <- data %>% dplyr::full_join(temp, by = c(\\id\\)) %>%\n  dplyr::mutate(diff_G1 = dTAG - DMSO,\n                  diff_async = AID - UT) %>%\n  dplyr::filter(id %in% regID)\n\n\n### P1. UT vs AID\nscore.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$UT, score.tb$AID)\np1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P1. DMSO vs dTAG\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$dTAG)\np2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. DMSO vs A485\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$A485)\np3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. DMSO vs UT\nscore.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$DMSO, score.tb$UT)\np4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\n### P3. dTAG vs AID\nscore.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$dTAG, score.tb$AID)\np5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n### P3. diff\nscore.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)\nscore.tb <- score.tb %>% dplyr::arrange(density)\ncorrelation <- cor(score.tb$diff_G1, score.tb$diff_async)\np6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +\n  geom_point() + \n  scale_color_viridis() +\n  xlim(-1, 0.5) + ylim(-1, 0.5) +\n  coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \\grey\\) +\n  theme_classic() + ggtitle(paste0(\\Consensus loop score\\)) +\n  annotate(\\text\\, x = -1, y = 0.5, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\)\n\n\npng(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.png\\)), res = 600, units = \\in\\, width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\nsvglite(here(figDir, paste0(\\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.svg\\)), width = 5*2.5, height = 2.5*2.5)\nprint(cowplot::plot_grid(p1, p2, p3, \n                         p4, p5, p6, align = \\h\\, ncol = 3))\ndev.off()\n\n\n```\n```"} -->
```r
```r
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure.bedpe\)) %>%
dplyr::mutate(res = V3 - V2,
id = paste(V1, res, V2, V5, sep = \_\))
regID <- temp$id
diffCutoff = 0.2
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- fread(here(consensusDir, \chromo_cons_score_async.tsv\)) %>%
dplyr::select(id, UT, AID)
score.tb <- data %>% dplyr::full_join(temp, by = c(\id\)) %>%
dplyr::mutate(diff_G1 = dTAG - DMSO,
diff_async = AID - UT) %>%
dplyr::filter(id %in% regID)
### P1. UT vs AID
score.tb$density <- get_density(score.tb$UT, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$UT, score.tb$AID)
p1 <- ggplot(score.tb, aes(x = UT, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P1. DMSO vs dTAG
score.tb$density <- get_density(score.tb$DMSO, score.tb$dTAG, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$dTAG)
p2 <- ggplot(score.tb, aes(x = DMSO, y = dTAG, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs A485
score.tb$density <- get_density(score.tb$DMSO, score.tb$A485, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$A485)
p3 <- ggplot(score.tb, aes(x = DMSO, y = A485, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. DMSO vs UT
score.tb$density <- get_density(score.tb$DMSO, score.tb$UT, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$DMSO, score.tb$UT)
p4 <- ggplot(score.tb, aes(x = DMSO, y = UT, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. dTAG vs AID
score.tb$density <- get_density(score.tb$dTAG, score.tb$AID, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$dTAG, score.tb$AID)
p5 <- ggplot(score.tb, aes(x = dTAG, y = AID, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
### P3. diff
score.tb$density <- get_density(score.tb$diff_G1, score.tb$diff_async, n = 100)
score.tb <- score.tb %>% dplyr::arrange(density)
correlation <- cor(score.tb$diff_G1, score.tb$diff_async)
p6 <- ggplot(score.tb, aes(x = diff_G1, y = diff_async, color = density)) +
geom_point() +
scale_color_viridis() +
xlim(-1, 0.5) + ylim(-1, 0.5) +
coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_hline(yintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = diffCutoff, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = -diffCutoff, alpha = 0.5, color = \grey\) +
theme_classic() + ggtitle(paste0(\Consensus loop score\)) +
annotate(\text\, x = -1, y = 0.5, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
png(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.png\)), res = 600, units = \in\, width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
svglite(here(figDir, paste0(\consensus_score_scatterplot_pu100pz100_G1vsAsync_str.svg\)), width = 5*2.5, height = 2.5*2.5)
print(cowplot::plot_grid(p1, p2, p3,
p4, p5, p6, align = \h\, ncol = 3))
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### reg loops
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# Importing loops. For make comparison easier, 25 kb\nbinSize = 25*1000\ntemp <- fread(here(refDir, \\Tjian_chromosight_loop.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloop.async <- importBedpe(temp)\n\n\nbinSize = 25*1000\ntemp <- fread(here(consensusDir, \\chromo_cons.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloop.G1 <- importBedpe(temp)\n\n\n# Check overlap\noverlap <- findOverlaps(loop.async, loop.G1)\n\n\nn.async <- nrow(as_tibble(loop.async))\nn.G1 <- nrow(as_tibble(loop.G1))\nn.async.overlap <- length(unique(queryHits(overlap)))\nn.G1.overlap <- length(unique(subjectHits(overlap)))\n\nplot(euler(c(\\Async\\ = n.async - n.async.overlap,\n             \\Async&G1\\ = n.async.overlap,\n             \\G1\\ = 1)), quantities = TRUE)\n\n\nplot(euler(c(\\Async\\ =1,\n             \\Async&G1\\ = n.G1.overlap,\n             \\G1\\ = n.G1 - n.G1.overlap)), quantities = TRUE)\n\n\n\n\n# Importing loops. For make comparison easier, 25 kb\nbinSize = 25*1000\ntemp.pp <- fread(here(refDir, \\Tjian_chromosight_loop_P-P.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp.pp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nbinSize = 25*1000\ntemp.ep <- fread(here(refDir, \\Tjian_chromosight_loop_E-P.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp.ep) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\ntemp <- bind_rows(temp.pp, temp.ep)\nloop.async <- importBedpe(temp)\n\n\nbinSize = 25*1000\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloop.G1 <- importBedpe(temp)\n\n\n# Check overlap\noverlap <- findOverlaps(loop.async, loop.G1)\n\n\nn.async <- nrow(as_tibble(loop.async))\nn.G1 <- nrow(as_tibble(loop.G1))\nn.async.overlap <- length(unique(queryHits(overlap)))\nn.G1.overlap <- length(unique(subjectHits(overlap)))\n\nplot(euler(c(\\Async\\ = n.async - n.async.overlap,\n             \\Async&G1\\ = n.async.overlap,\n             \\G1\\ = 1)), quantities = TRUE)\n\n\nplot(euler(c(\\Async\\ =0.1,\n             \\Async&G1\\ = n.G1.overlap,\n             \\G1\\ = n.G1 - n.G1.overlap)), quantities = TRUE)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# Importing loops. For make comparison easier, 25 kb
binSize = 25*1000
temp <- fread(here(refDir, \Tjian_chromosight_loop.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loop.async <- importBedpe(temp)
binSize = 25*1000
temp <- fread(here(consensusDir, \chromo_cons.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loop.G1 <- importBedpe(temp)
# Check overlap
overlap <- findOverlaps(loop.async, loop.G1)
n.async <- nrow(as_tibble(loop.async))
n.G1 <- nrow(as_tibble(loop.G1))
n.async.overlap <- length(unique(queryHits(overlap)))
n.G1.overlap <- length(unique(subjectHits(overlap)))
plot(euler(c(\Async\ = n.async - n.async.overlap,
\Async&G1\ = n.async.overlap,
\G1\ = 1)), quantities = TRUE)
plot(euler(c(\Async\ =1,
\Async&G1\ = n.G1.overlap,
\G1\ = n.G1 - n.G1.overlap)), quantities = TRUE)
# Importing loops. For make comparison easier, 25 kb
binSize = 25*1000
temp.pp <- fread(here(refDir, \Tjian_chromosight_loop_P-P.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp.pp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
binSize = 25*1000
temp.ep <- fread(here(refDir, \Tjian_chromosight_loop_E-P.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp.ep) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
temp <- bind_rows(temp.pp, temp.ep)
loop.async <- importBedpe(temp)
binSize = 25*1000
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loop.G1 <- importBedpe(temp)
# Check overlap
overlap <- findOverlaps(loop.async, loop.G1)
n.async <- nrow(as_tibble(loop.async))
n.G1 <- nrow(as_tibble(loop.G1))
n.async.overlap <- length(unique(queryHits(overlap)))
n.G1.overlap <- length(unique(subjectHits(overlap)))
plot(euler(c(\Async\ = n.async - n.async.overlap,
\Async&G1\ = n.async.overlap,
\G1\ = 1)), quantities = TRUE)
plot(euler(c(\Async\ =0.1,
\Async&G1\ = n.G1.overlap,
\G1\ = n.G1 - n.G1.overlap)), quantities = TRUE)
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-->\n\n```r\n```r\n# Importing loops. For make comparison easier, 25 kb\nbinSize = 25*1000\ntemp <- fread(here(refDir, \\Tjian_chromosight_loop.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloop.async <- importBedpe(temp)\n\n\nbinSize = 25*1000\ntemp <- fread(here(consensusDir, \\chromo_cons.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloop.G1 <- importBedpe(temp)\n\n\n# Check overlap\noverlap <- findOverlaps(loop.async, loop.G1)\n\n\nn.async <- nrow(as_tibble(loop.async))\nn.G1 <- nrow(as_tibble(loop.G1))\nn.async.overlap <- length(unique(queryHits(overlap)))\nn.G1.overlap <- length(unique(subjectHits(overlap)))\n\nplot(euler(c(\\Async\\ = n.async - n.async.overlap,\n             \\Async&G1\\ = n.async.overlap,\n             \\G1\\ = 1)), quantities = TRUE)\n\n\nplot(euler(c(\\Async\\ =1,\n             \\Async&G1\\ = n.G1.overlap,\n             \\G1\\ = n.G1 - n.G1.overlap)), quantities = TRUE)\n\n\n\n\n# Importing loops. For make comparison easier, 25 kb\nbinSize = 25*1000\ntemp.pp <- fread(here(refDir, \\Tjian_chromosight_loop_P-P.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp.pp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nbinSize = 25*1000\ntemp.ep <- fread(here(refDir, \\Tjian_chromosight_loop_E-P.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp.ep) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\ntemp <- bind_rows(temp.pp, temp.ep)\nloop.async <- importBedpe(temp)\n\n\nbinSize = 25*1000\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\)) %>%\n  dplyr::mutate(center1 = 0.5*(V2 + V3),\n                center2 = 0.5*(V5 + V6),\n                start1 = center1 - 0.5*binSize,\n                end1 = center1 + 0.5*binSize,\n                start2 = center2 - 0.5*binSize,\n                end2 = center2 + 0.5*binSize) %>%\n  dplyr::select(V1, start1, end1, V4, start2, end2)\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloop.G1 <- importBedpe(temp)\n\n\n# Check overlap\noverlap <- findOverlaps(loop.async, loop.G1)\n\n\nn.async <- nrow(as_tibble(loop.async))\nn.G1 <- nrow(as_tibble(loop.G1))\nn.async.overlap <- length(unique(queryHits(overlap)))\nn.G1.overlap <- length(unique(subjectHits(overlap)))\n\nplot(euler(c(\\Async\\ = n.async - n.async.overlap,\n             \\Async&G1\\ = n.async.overlap,\n             \\G1\\ = 1)), quantities = TRUE)\n\n\nplot(euler(c(\\Async\\ =0.1,\n             \\Async&G1\\ = n.G1.overlap,\n             \\G1\\ = n.G1 - n.G1.overlap)), quantities = TRUE)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
# Importing loops. For make comparison easier, 25 kb
binSize = 25*1000
temp <- fread(here(refDir, \Tjian_chromosight_loop.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loop.async <- importBedpe(temp)
binSize = 25*1000
temp <- fread(here(consensusDir, \chromo_cons.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loop.G1 <- importBedpe(temp)
# Check overlap
overlap <- findOverlaps(loop.async, loop.G1)
n.async <- nrow(as_tibble(loop.async))
n.G1 <- nrow(as_tibble(loop.G1))
n.async.overlap <- length(unique(queryHits(overlap)))
n.G1.overlap <- length(unique(subjectHits(overlap)))
plot(euler(c(\Async\ = n.async - n.async.overlap,
\Async&G1\ = n.async.overlap,
\G1\ = 1)), quantities = TRUE)
plot(euler(c(\Async\ =1,
\Async&G1\ = n.G1.overlap,
\G1\ = n.G1 - n.G1.overlap)), quantities = TRUE)
# Importing loops. For make comparison easier, 25 kb
binSize = 25*1000
temp.pp <- fread(here(refDir, \Tjian_chromosight_loop_P-P.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp.pp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
binSize = 25*1000
temp.ep <- fread(here(refDir, \Tjian_chromosight_loop_E-P.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp.ep) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
temp <- bind_rows(temp.pp, temp.ep)
loop.async <- importBedpe(temp)
binSize = 25*1000
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\)) %>%
dplyr::mutate(center1 = 0.5*(V2 + V3),
center2 = 0.5*(V5 + V6),
start1 = center1 - 0.5*binSize,
end1 = center1 + 0.5*binSize,
start2 = center2 - 0.5*binSize,
end2 = center2 + 0.5*binSize) %>%
dplyr::select(V1, start1, end1, V4, start2, end2)
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loop.G1 <- importBedpe(temp)
# Check overlap
overlap <- findOverlaps(loop.async, loop.G1)
n.async <- nrow(as_tibble(loop.async))
n.G1 <- nrow(as_tibble(loop.G1))
n.async.overlap <- length(unique(queryHits(overlap)))
n.G1.overlap <- length(unique(subjectHits(overlap)))
plot(euler(c(\Async\ = n.async - n.async.overlap,
\Async&G1\ = n.async.overlap,
\G1\ = 1)), quantities = TRUE)
plot(euler(c(\Async\ =0.1,
\Async&G1\ = n.G1.overlap,
\G1\ = n.G1 - n.G1.overlap)), quantities = TRUE)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### str loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
#### Importing ChIP-exo peaks
refDir <- here(\../..\, \reference\)
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
peak.H3K4me3 <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
peak.CTCF <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
peak.RAD21 <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
peak.Whyte.SE <- importPeak(here(refDir, \superEnhancer_Whyte_ESC_mm10.bed\))
peak.Dylan.SE <- importPeak(here(refDir, \superEnhancer_Dylan_ESC.bed\))
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
#### Importing ChIP-exo peaks
refDir <- here(\../..\, \reference\)
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
peak.H3K4me3 <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
peak.CTCF <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
peak.RAD21 <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
peak.Whyte.SE <- importPeak(here(refDir, \superEnhancer_Whyte_ESC_mm10.bed\))
peak.Dylan.SE <- importPeak(here(refDir, \superEnhancer_Dylan_ESC.bed\))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Comparing loops called from Async
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ncreateLoopAnnotation <- function(bedpe.loop.anno, name, figDir, outDir, colorList){\n  temp = bedpe.loop.anno %>%\n    dplyr::mutate(sample = name)\n  \n  num = nrow(temp)\n  p7 = ggplot(temp, aes(x = sample, fill = Anno2)) +\n    geom_bar(color = \\black\\) +\n    theme_bw() +\n    labs(title = paste0(num, \\ loops\\),\n         x = \\\\, y = \\Counts\\) +\n    scale_y_continuous(labels = comma_format()) +\n    theme(plot.title = element_text(hjust = 0.5),\n          aspect.ratio = 5,\n          legend.position = \\right\\,\n          legend.direction  = \\vertical\\) +\n    scale_fill_manual(values = colorList)\n  \n  width = 3\n  height = 5\n  svglite(here(figDir,\n               paste0(\\loopClassify_\\, name, \\.svg\\)),\n          width = width, height = height)\n  plot(p7)\n  invisible(dev.off())\n  png(here(figDir,\n           paste0(\\loopClassify_\\, name, \\.png\\)),\n      width = width, height = height, res = 600, units = \\in\\)\n  plot(p7)\n  invisible(dev.off())\n}\n\n\nannotateLoopRelaxedTSS <- function(bedpe.anno){\n  temp = bedpe.anno %>% dplyr::rowwise() %>%\n    dplyr::mutate(\n      A1 = if_else((A1_H3K4me3TSS), \\P\\, \n                   if_else((A1_H3K27ac), \\E\\,\n                           if_else((A1_CTCF|A1_RAD21), \\S\\, \\X\\))),\n      A2 = if_else((A2_H3K4me3TSS), \\P\\, \n                   if_else((A2_H3K27ac), \\E\\,\n                           if_else((A2_CTCF|A2_RAD21), \\S\\, \\X\\)))\n    )\n  temp = temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(Anno = paste0(A1, \\-\\, A2),\n                  Anno2 = if_else(Anno == \\E-P\\, \\P-E\\, \n                                  if_else(Anno == \\S-P\\, \\P-S\\,\n                                          if_else(Anno == \\X-P\\, \\P-X\\,\n                                                  if_else(Anno == \\S-E\\, \\E-S\\,\n                                                          if_else(Anno == \\X-E\\, \\E-X\\,\n                                                                  if_else(Anno == \\X-S\\, \\S-X\\,\n                                                                          Anno)))))),\n    )\n  \n  temp$Anno2 = factor(temp$Anno2, level = c(\\X-X\\,\n                                            \\S-X\\, \\S-S\\,\n                                            \\E-X\\,\\E-S\\,\\E-E\\,\n                                            \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\))\n  \n  # Checking the precense of super enhancer\n  temp <- temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(AnnoSE = ifelse(A1_Whyte.SE | A2_Whyte.SE, \\SE\\, \\NO\\))\n  temp$AnnoSE <- factor(temp$AnnoSE, level = c(\\SE\\, \\NO\\))\n  \n  return(temp)\n}\n\nannotateLoopPromoterTSS <- function(bedpe.anno){\n  temp = bedpe.anno %>% dplyr::rowwise() %>%\n    dplyr::mutate(\n      A1 = if_else((A1_H3K4me3TSS), \\P\\, \\N\\),\n      A2 = if_else((A2_H3K4me3TSS), \\P\\, \\N\\)\n    )\n  temp = temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(Anno = paste0(A1, \\-\\, A2),\n                  Anno2 = if_else(Anno == \\N-P\\, \\P-N\\, Anno))\n  \n  temp$Anno2 = factor(temp$Anno2, level = c(\\N-N\\, \\P-N\\, \\P-P\\))\n  \n  return(temp)\n}\n\nannotateLoopEnhancer <- function(bedpe.anno){\n  temp = bedpe.anno %>% dplyr::rowwise() %>%\n    dplyr::mutate(\n      A1 = if_else((A1_H3K27ac), \\E\\, \\N\\),\n      A2 = if_else((A2_H3K27ac), \\E\\, \\N\\)\n    )\n  temp = temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(Anno = paste0(A1, \\-\\, A2),\n                  Anno2 = if_else(Anno == \\N-E\\, \\E-N\\, Anno))\n  temp$Anno2 = factor(temp$Anno2, level = c(\\N-N\\, \\E-N\\, \\E-E\\))\n  \n  return(temp)\n}\n\nannotateLoopStructure <- function(bedpe.anno){\n  temp = bedpe.anno %>% dplyr::rowwise() %>%\n    dplyr::mutate(\n      A1 = if_else((A1_CTCF|A1_RAD21), \\S\\, \\N\\),\n      A2 = if_else((A2_CTCF|A2_RAD21), \\S\\, \\N\\)\n    )\n  temp = temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(Anno = paste0(A1, \\-\\, A2),\n                  Anno2 = if_else(Anno == \\N-S\\, \\S-N\\, Anno))\n  temp$Anno2 = factor(temp$Anno2, level = c(\\N-N\\, \\S-N\\, \\S-S\\))\n  return(temp)\n}\n\n\nannotateAnchorTSS <- function(bedpe){\n  tb.loop = \n    setOverlapColumn(\\Whyte.SE\\,\n                     setOverlapColumn(\\CTCF\\,\n                                      setOverlapColumn(\\RAD21\\,\n                                                       setOverlapColumn(\\H3K27ac\\,\n                                                                        setOverlapColumn(\\H3K4me3TSS\\, bedpe)))))\n  return(tb.loop)\n}\n\nsetOverlapColumn <- function(peakName, loop){\n  tb.loop = as_tibble(loop)\n  overlap = returnOverlapIndexLixt(get(paste0(\\peak.\\, peakName)), tb.loop)\n  tb.loop[[paste0(\\A1_\\, peakName)]] = FALSE\n  tb.loop[[paste0(\\A1_\\, peakName)]][overlap[[1]]] = TRUE\n  tb.loop[[paste0(\\A2_\\, peakName)]] = FALSE\n  tb.loop[[paste0(\\A2_\\, peakName)]][overlap[[2]]] = TRUE\n  return(tb.loop)\n}\n\nreturnOverlapIndexLixt <- function(peak, loop){\n  anchor1.tb = as_tibble(loop) %>% dplyr::select(chrom1, start1, end1)\n  anchor1 = makeGRangesFromDataFrame(data.frame(\n    chr = anchor1.tb$chrom1,\n    start = anchor1.tb$start1,\n    end = anchor1.tb$end1\n  ))\n  \n  anchor2.tb = as_tibble(loop) %>% dplyr::select(chrom2, start2, end2)\n  anchor2 = makeGRangesFromDataFrame(data.frame(\n    chr = anchor2.tb$chrom2,\n    start = anchor2.tb$start2,\n    end = anchor2.tb$end2\n  ))\n  \n  overlap = list(overlap1 = unique(queryHits(findOverlaps(anchor1, peak))),\n                 overlap2 = unique(queryHits(findOverlaps(anchor2, peak))))\n  return(overlap)\n}\n\nsaveAnnoGroupBedpe <- function(temp, anno.list, name, annoName, ouDir){\n  loop = temp %>% dplyr::filter(Anno2 %in% anno.list) %>%\n    dplyr::select(c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\))\n  fwrite(loop, here(outDir, paste0(name, \\_\\, annoName, \\.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n}\n```\n\n<!-- rnb-source-end -->\n"} -->
createLoopAnnotation <- function(bedpe.loop.anno, name, figDir, outDir, colorList){
temp = bedpe.loop.anno %>%
dplyr::mutate(sample = name)
num = nrow(temp)
p7 = ggplot(temp, aes(x = sample, fill = Anno2)) +
geom_bar(color = \black\) +
theme_bw() +
labs(title = paste0(num, \ loops\),
x = \\, y = \Counts\) +
scale_y_continuous(labels = comma_format()) +
theme(plot.title = element_text(hjust = 0.5),
aspect.ratio = 5,
legend.position = \right\,
legend.direction = \vertical\) +
scale_fill_manual(values = colorList)
width = 3
height = 5
svglite(here(figDir,
paste0(\loopClassify_\, name, \.svg\)),
width = width, height = height)
plot(p7)
invisible(dev.off())
png(here(figDir,
paste0(\loopClassify_\, name, \.png\)),
width = width, height = height, res = 600, units = \in\)
plot(p7)
invisible(dev.off())
}
annotateLoopRelaxedTSS <- function(bedpe.anno){
temp = bedpe.anno %>% dplyr::rowwise() %>%
dplyr::mutate(
A1 = if_else((A1_H3K4me3TSS), \P\,
if_else((A1_H3K27ac), \E\,
if_else((A1_CTCF|A1_RAD21), \S\, \X\))),
A2 = if_else((A2_H3K4me3TSS), \P\,
if_else((A2_H3K27ac), \E\,
if_else((A2_CTCF|A2_RAD21), \S\, \X\)))
)
temp = temp %>% dplyr::rowwise() %>%
dplyr::mutate(Anno = paste0(A1, \-\, A2),
Anno2 = if_else(Anno == \E-P\, \P-E\,
if_else(Anno == \S-P\, \P-S\,
if_else(Anno == \X-P\, \P-X\,
if_else(Anno == \S-E\, \E-S\,
if_else(Anno == \X-E\, \E-X\,
if_else(Anno == \X-S\, \S-X\,
Anno)))))),
)
temp$Anno2 = factor(temp$Anno2, level = c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\))
# Checking the precense of super enhancer
temp <- temp %>% dplyr::rowwise() %>%
dplyr::mutate(AnnoSE = ifelse(A1_Whyte.SE | A2_Whyte.SE, \SE\, \NO\))
temp$AnnoSE <- factor(temp$AnnoSE, level = c(\SE\, \NO\))
return(temp)
}
annotateLoopPromoterTSS <- function(bedpe.anno){
temp = bedpe.anno %>% dplyr::rowwise() %>%
dplyr::mutate(
A1 = if_else((A1_H3K4me3TSS), \P\, \N\),
A2 = if_else((A2_H3K4me3TSS), \P\, \N\)
)
temp = temp %>% dplyr::rowwise() %>%
dplyr::mutate(Anno = paste0(A1, \-\, A2),
Anno2 = if_else(Anno == \N-P\, \P-N\, Anno))
temp$Anno2 = factor(temp$Anno2, level = c(\N-N\, \P-N\, \P-P\))
return(temp)
}
annotateLoopEnhancer <- function(bedpe.anno){
temp = bedpe.anno %>% dplyr::rowwise() %>%
dplyr::mutate(
A1 = if_else((A1_H3K27ac), \E\, \N\),
A2 = if_else((A2_H3K27ac), \E\, \N\)
)
temp = temp %>% dplyr::rowwise() %>%
dplyr::mutate(Anno = paste0(A1, \-\, A2),
Anno2 = if_else(Anno == \N-E\, \E-N\, Anno))
temp$Anno2 = factor(temp$Anno2, level = c(\N-N\, \E-N\, \E-E\))
return(temp)
}
annotateLoopStructure <- function(bedpe.anno){
temp = bedpe.anno %>% dplyr::rowwise() %>%
dplyr::mutate(
A1 = if_else((A1_CTCF|A1_RAD21), \S\, \N\),
A2 = if_else((A2_CTCF|A2_RAD21), \S\, \N\)
)
temp = temp %>% dplyr::rowwise() %>%
dplyr::mutate(Anno = paste0(A1, \-\, A2),
Anno2 = if_else(Anno == \N-S\, \S-N\, Anno))
temp$Anno2 = factor(temp$Anno2, level = c(\N-N\, \S-N\, \S-S\))
return(temp)
}
annotateAnchorTSS <- function(bedpe){
tb.loop =
setOverlapColumn(\Whyte.SE\,
setOverlapColumn(\CTCF\,
setOverlapColumn(\RAD21\,
setOverlapColumn(\H3K27ac\,
setOverlapColumn(\H3K4me3TSS\, bedpe)))))
return(tb.loop)
}
setOverlapColumn <- function(peakName, loop){
tb.loop = as_tibble(loop)
overlap = returnOverlapIndexLixt(get(paste0(\peak.\, peakName)), tb.loop)
tb.loop[[paste0(\A1_\, peakName)]] = FALSE
tb.loop[[paste0(\A1_\, peakName)]][overlap[[1]]] = TRUE
tb.loop[[paste0(\A2_\, peakName)]] = FALSE
tb.loop[[paste0(\A2_\, peakName)]][overlap[[2]]] = TRUE
return(tb.loop)
}
returnOverlapIndexLixt <- function(peak, loop){
anchor1.tb = as_tibble(loop) %>% dplyr::select(chrom1, start1, end1)
anchor1 = makeGRangesFromDataFrame(data.frame(
chr = anchor1.tb$chrom1,
start = anchor1.tb$start1,
end = anchor1.tb$end1
))
anchor2.tb = as_tibble(loop) %>% dplyr::select(chrom2, start2, end2)
anchor2 = makeGRangesFromDataFrame(data.frame(
chr = anchor2.tb$chrom2,
start = anchor2.tb$start2,
end = anchor2.tb$end2
))
overlap = list(overlap1 = unique(queryHits(findOverlaps(anchor1, peak))),
overlap2 = unique(queryHits(findOverlaps(anchor2, peak))))
return(overlap)
}
saveAnnoGroupBedpe <- function(temp, anno.list, name, annoName, ouDir){
loop = temp %>% dplyr::filter(Anno2 %in% anno.list) %>%
dplyr::select(c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\))
fwrite(loop, here(outDir, paste0(name, \_\, annoName, \.bedpe\)), sep = \\t\, col.names = FALSE)
}
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ncreateLoopAnnotation <- function(bedpe.loop.anno, name, figDir, outDir, colorList){\n  temp = bedpe.loop.anno %>%\n    dplyr::mutate(sample = name)\n  \n  num = nrow(temp)\n  p7 = ggplot(temp, aes(x = sample, fill = Anno2)) +\n    geom_bar(color = \\black\\) +\n    theme_bw() +\n    labs(title = paste0(num, \\ loops\\),\n         x = \\\\, y = \\Counts\\) +\n    scale_y_continuous(labels = comma_format()) +\n    theme(plot.title = element_text(hjust = 0.5),\n          aspect.ratio = 5,\n          legend.position = \\right\\,\n          legend.direction  = \\vertical\\) +\n    scale_fill_manual(values = colorList)\n  \n  width = 3\n  height = 5\n  svglite(here(figDir,\n               paste0(\\loopClassify_\\, name, \\.svg\\)),\n          width = width, height = height)\n  plot(p7)\n  invisible(dev.off())\n  png(here(figDir,\n           paste0(\\loopClassify_\\, name, \\.png\\)),\n      width = width, height = height, res = 600, units = \\in\\)\n  plot(p7)\n  invisible(dev.off())\n}\n\n\nannotateLoopRelaxedTSS <- function(bedpe.anno){\n  temp = bedpe.anno %>% dplyr::rowwise() %>%\n    dplyr::mutate(\n      A1 = if_else((A1_H3K4me3TSS), \\P\\, \n                   if_else((A1_H3K27ac), \\E\\,\n                           if_else((A1_CTCF|A1_RAD21), \\S\\, \\X\\))),\n      A2 = if_else((A2_H3K4me3TSS), \\P\\, \n                   if_else((A2_H3K27ac), \\E\\,\n                           if_else((A2_CTCF|A2_RAD21), \\S\\, \\X\\)))\n    )\n  temp = temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(Anno = paste0(A1, \\-\\, A2),\n                  Anno2 = if_else(Anno == \\E-P\\, \\P-E\\, \n                                  if_else(Anno == \\S-P\\, \\P-S\\,\n                                          if_else(Anno == \\X-P\\, \\P-X\\,\n                                                  if_else(Anno == \\S-E\\, \\E-S\\,\n                                                          if_else(Anno == \\X-E\\, \\E-X\\,\n                                                                  if_else(Anno == \\X-S\\, \\S-X\\,\n                                                                          Anno)))))),\n    )\n  \n  temp$Anno2 = factor(temp$Anno2, level = c(\\X-X\\,\n                                            \\S-X\\, \\S-S\\,\n                                            \\E-X\\,\\E-S\\,\\E-E\\,\n                                            \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\))\n  \n  # Checking the precense of super enhancer\n  temp <- temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(AnnoSE = ifelse(A1_Whyte.SE | A2_Whyte.SE, \\SE\\, \\NO\\))\n  temp$AnnoSE <- factor(temp$AnnoSE, level = c(\\SE\\, \\NO\\))\n  \n  return(temp)\n}\n\nannotateLoopPromoterTSS <- function(bedpe.anno){\n  temp = bedpe.anno %>% dplyr::rowwise() %>%\n    dplyr::mutate(\n      A1 = if_else((A1_H3K4me3TSS), \\P\\, \\N\\),\n      A2 = if_else((A2_H3K4me3TSS), \\P\\, \\N\\)\n    )\n  temp = temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(Anno = paste0(A1, \\-\\, A2),\n                  Anno2 = if_else(Anno == \\N-P\\, \\P-N\\, Anno))\n  \n  temp$Anno2 = factor(temp$Anno2, level = c(\\N-N\\, \\P-N\\, \\P-P\\))\n  \n  return(temp)\n}\n\nannotateLoopEnhancer <- function(bedpe.anno){\n  temp = bedpe.anno %>% dplyr::rowwise() %>%\n    dplyr::mutate(\n      A1 = if_else((A1_H3K27ac), \\E\\, \\N\\),\n      A2 = if_else((A2_H3K27ac), \\E\\, \\N\\)\n    )\n  temp = temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(Anno = paste0(A1, \\-\\, A2),\n                  Anno2 = if_else(Anno == \\N-E\\, \\E-N\\, Anno))\n  temp$Anno2 = factor(temp$Anno2, level = c(\\N-N\\, \\E-N\\, \\E-E\\))\n  \n  return(temp)\n}\n\nannotateLoopStructure <- function(bedpe.anno){\n  temp = bedpe.anno %>% dplyr::rowwise() %>%\n    dplyr::mutate(\n      A1 = if_else((A1_CTCF|A1_RAD21), \\S\\, \\N\\),\n      A2 = if_else((A2_CTCF|A2_RAD21), \\S\\, \\N\\)\n    )\n  temp = temp %>% dplyr::rowwise() %>%\n    dplyr::mutate(Anno = paste0(A1, \\-\\, A2),\n                  Anno2 = if_else(Anno == \\N-S\\, \\S-N\\, Anno))\n  temp$Anno2 = factor(temp$Anno2, level = c(\\N-N\\, \\S-N\\, \\S-S\\))\n  return(temp)\n}\n\n\nannotateAnchorTSS <- function(bedpe){\n  tb.loop = \n    setOverlapColumn(\\Whyte.SE\\,\n                     setOverlapColumn(\\CTCF\\,\n                                      setOverlapColumn(\\RAD21\\,\n                                                       setOverlapColumn(\\H3K27ac\\,\n                                                                        setOverlapColumn(\\H3K4me3TSS\\, bedpe)))))\n  return(tb.loop)\n}\n\nsetOverlapColumn <- function(peakName, loop){\n  tb.loop = as_tibble(loop)\n  overlap = returnOverlapIndexLixt(get(paste0(\\peak.\\, peakName)), tb.loop)\n  tb.loop[[paste0(\\A1_\\, peakName)]] = FALSE\n  tb.loop[[paste0(\\A1_\\, peakName)]][overlap[[1]]] = TRUE\n  tb.loop[[paste0(\\A2_\\, peakName)]] = FALSE\n  tb.loop[[paste0(\\A2_\\, peakName)]][overlap[[2]]] = TRUE\n  return(tb.loop)\n}\n\nreturnOverlapIndexLixt <- function(peak, loop){\n  anchor1.tb = as_tibble(loop) %>% dplyr::select(chrom1, start1, end1)\n  anchor1 = makeGRangesFromDataFrame(data.frame(\n    chr = anchor1.tb$chrom1,\n    start = anchor1.tb$start1,\n    end = anchor1.tb$end1\n  ))\n  \n  anchor2.tb = as_tibble(loop) %>% dplyr::select(chrom2, start2, end2)\n  anchor2 = makeGRangesFromDataFrame(data.frame(\n    chr = anchor2.tb$chrom2,\n    start = anchor2.tb$start2,\n    end = anchor2.tb$end2\n  ))\n  \n  overlap = list(overlap1 = unique(queryHits(findOverlaps(anchor1, peak))),\n                 overlap2 = unique(queryHits(findOverlaps(anchor2, peak))))\n  return(overlap)\n}\n\nsaveAnnoGroupBedpe <- function(temp, anno.list, name, annoName, ouDir){\n  loop = temp %>% dplyr::filter(Anno2 %in% anno.list) %>%\n    dplyr::select(c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\))\n  fwrite(loop, here(outDir, paste0(name, \\_\\, annoName, \\.bedpe\\)), sep = \\\\t\\, col.names = FALSE)\n}\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
createLoopAnnotation <- function(bedpe.loop.anno, name, figDir, outDir, colorList){
temp = bedpe.loop.anno %>%
dplyr::mutate(sample = name)
num = nrow(temp)
p7 = ggplot(temp, aes(x = sample, fill = Anno2)) +
geom_bar(color = \black\) +
theme_bw() +
labs(title = paste0(num, \ loops\),
x = \\, y = \Counts\) +
scale_y_continuous(labels = comma_format()) +
theme(plot.title = element_text(hjust = 0.5),
aspect.ratio = 5,
legend.position = \right\,
legend.direction = \vertical\) +
scale_fill_manual(values = colorList)
width = 3
height = 5
svglite(here(figDir,
paste0(\loopClassify_\, name, \.svg\)),
width = width, height = height)
plot(p7)
invisible(dev.off())
png(here(figDir,
paste0(\loopClassify_\, name, \.png\)),
width = width, height = height, res = 600, units = \in\)
plot(p7)
invisible(dev.off())
}
annotateLoopRelaxedTSS <- function(bedpe.anno){
temp = bedpe.anno %>% dplyr::rowwise() %>%
dplyr::mutate(
A1 = if_else((A1_H3K4me3TSS), \P\,
if_else((A1_H3K27ac), \E\,
if_else((A1_CTCF|A1_RAD21), \S\, \X\))),
A2 = if_else((A2_H3K4me3TSS), \P\,
if_else((A2_H3K27ac), \E\,
if_else((A2_CTCF|A2_RAD21), \S\, \X\)))
)
temp = temp %>% dplyr::rowwise() %>%
dplyr::mutate(Anno = paste0(A1, \-\, A2),
Anno2 = if_else(Anno == \E-P\, \P-E\,
if_else(Anno == \S-P\, \P-S\,
if_else(Anno == \X-P\, \P-X\,
if_else(Anno == \S-E\, \E-S\,
if_else(Anno == \X-E\, \E-X\,
if_else(Anno == \X-S\, \S-X\,
Anno)))))),
)
temp$Anno2 = factor(temp$Anno2, level = c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\))
# Checking the precense of super enhancer
temp <- temp %>% dplyr::rowwise() %>%
dplyr::mutate(AnnoSE = ifelse(A1_Whyte.SE | A2_Whyte.SE, \SE\, \NO\))
temp$AnnoSE <- factor(temp$AnnoSE, level = c(\SE\, \NO\))
return(temp)
}
annotateLoopPromoterTSS <- function(bedpe.anno){
temp = bedpe.anno %>% dplyr::rowwise() %>%
dplyr::mutate(
A1 = if_else((A1_H3K4me3TSS), \P\, \N\),
A2 = if_else((A2_H3K4me3TSS), \P\, \N\)
)
temp = temp %>% dplyr::rowwise() %>%
dplyr::mutate(Anno = paste0(A1, \-\, A2),
Anno2 = if_else(Anno == \N-P\, \P-N\, Anno))
temp$Anno2 = factor(temp$Anno2, level = c(\N-N\, \P-N\, \P-P\))
return(temp)
}
annotateLoopEnhancer <- function(bedpe.anno){
temp = bedpe.anno %>% dplyr::rowwise() %>%
dplyr::mutate(
A1 = if_else((A1_H3K27ac), \E\, \N\),
A2 = if_else((A2_H3K27ac), \E\, \N\)
)
temp = temp %>% dplyr::rowwise() %>%
dplyr::mutate(Anno = paste0(A1, \-\, A2),
Anno2 = if_else(Anno == \N-E\, \E-N\, Anno))
temp$Anno2 = factor(temp$Anno2, level = c(\N-N\, \E-N\, \E-E\))
return(temp)
}
annotateLoopStructure <- function(bedpe.anno){
temp = bedpe.anno %>% dplyr::rowwise() %>%
dplyr::mutate(
A1 = if_else((A1_CTCF|A1_RAD21), \S\, \N\),
A2 = if_else((A2_CTCF|A2_RAD21), \S\, \N\)
)
temp = temp %>% dplyr::rowwise() %>%
dplyr::mutate(Anno = paste0(A1, \-\, A2),
Anno2 = if_else(Anno == \N-S\, \S-N\, Anno))
temp$Anno2 = factor(temp$Anno2, level = c(\N-N\, \S-N\, \S-S\))
return(temp)
}
annotateAnchorTSS <- function(bedpe){
tb.loop =
setOverlapColumn(\Whyte.SE\,
setOverlapColumn(\CTCF\,
setOverlapColumn(\RAD21\,
setOverlapColumn(\H3K27ac\,
setOverlapColumn(\H3K4me3TSS\, bedpe)))))
return(tb.loop)
}
setOverlapColumn <- function(peakName, loop){
tb.loop = as_tibble(loop)
overlap = returnOverlapIndexLixt(get(paste0(\peak.\, peakName)), tb.loop)
tb.loop[[paste0(\A1_\, peakName)]] = FALSE
tb.loop[[paste0(\A1_\, peakName)]][overlap[[1]]] = TRUE
tb.loop[[paste0(\A2_\, peakName)]] = FALSE
tb.loop[[paste0(\A2_\, peakName)]][overlap[[2]]] = TRUE
return(tb.loop)
}
returnOverlapIndexLixt <- function(peak, loop){
anchor1.tb = as_tibble(loop) %>% dplyr::select(chrom1, start1, end1)
anchor1 = makeGRangesFromDataFrame(data.frame(
chr = anchor1.tb$chrom1,
start = anchor1.tb$start1,
end = anchor1.tb$end1
))
anchor2.tb = as_tibble(loop) %>% dplyr::select(chrom2, start2, end2)
anchor2 = makeGRangesFromDataFrame(data.frame(
chr = anchor2.tb$chrom2,
start = anchor2.tb$start2,
end = anchor2.tb$end2
))
overlap = list(overlap1 = unique(queryHits(findOverlaps(anchor1, peak))),
overlap2 = unique(queryHits(findOverlaps(anchor2, peak))))
return(overlap)
}
saveAnnoGroupBedpe <- function(temp, anno.list, name, annoName, ouDir){
loop = temp %>% dplyr::filter(Anno2 %in% anno.list) %>%
dplyr::select(c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\))
fwrite(loop, here(outDir, paste0(name, \_\, annoName, \.bedpe\)), sep = \\t\, col.names = FALSE)
}
```
```
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<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
### [2.5] Consensus loop annotation
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nconsensus.loop.tb <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\n################################################################################\n# Filtering H3K4me3 peaks that has TSS nearby\n# Since the finest resolution is 5kb, +-2.5kb will be used as a cutoff for checking TSS presence\nflankSize <- 2500\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\)\nTSS1kb.gr <- makeGRangesFromDataFrame(gene.tb)\ntemp <- peak.H3K4me3[unique(queryHits(findOverlaps(peak.H3K4me3, TSS1kb.gr)))]\nfwrite(as_tibble(temp), here(refDir, \n                             paste0(\\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.\\, \n                                    flankSize/1000, \\kbTSS.bed\\)), sep = \\\\t\\, col.names = FALSE)\npeak.H3K4me3TSS <- importPeak(here(refDir, paste0(\\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.\\, \n                                    flankSize/1000, \\kbTSS.bed\\)))\n################################################################################\n# Annotating with strict priority (P-TSS > E > S)\ntemp.anno.TSS <- annotateAnchorTSS(consensus.loop.tb) %>%\n  dplyr::mutate(\n    diff_dTAG_DMSO = (dTAG-DMSO),\n    diff_A485_DMSO = (A485-DMSO))\n\nname <- \\chromo_cons_annoHierarchy\\\nconsensus.loop.anno.tb <- annotateLoopRelaxedTSS(temp.anno.TSS)\nfwrite(consensus.loop.anno.tb, here(consensusDir, paste0(name, \\.tsv\\)), \n       sep = \\\\t\\, col.names = TRUE)\ncreateLoopAnnotation(consensus.loop.anno.tb, name, figDir, outDir,  colorListLoop)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, unique(consensus.loop.anno.tb$Anno2) , name, \\all\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\, \\E-E\\, \\E-S\\, \\E-X\\), name, \\regulatory\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\S-S\\, \\S-X\\), name, \\structure\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\X-X\\), name, \\x-x\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\), name, \\p-n\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\, \\P-E\\, \\E-E\\), name, \\pe-pe\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\, \\P-E\\), name, \\p-pe\\, outDir)\n\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\), name, \\p-p\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-E\\), name, \\p-e\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-S\\), name, \\p-s\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-X\\), name, \\p-x\\, outDir)\n\n################################################################################\n# Annotating with one marker (Promoter)\nname <- \\chromo_cons_annoPromoter\\\nconsensus.loop.anno.promoter.tb <- annotateLoopPromoterTSS(temp.anno.TSS)\nfwrite(consensus.loop.anno.promoter.tb, here(consensusDir, paste0(name, \\.tsv\\)), \n       sep = \\\\t\\, col.names = TRUE)\ncreateLoopAnnotation(consensus.loop.anno.promoter.tb, name, \n                     figDir, outDir,  colorListPromoter)\nsaveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, unique(consensus.loop.anno.promoter.tb$Anno2) , name, \\all\\, outDir)  \nsaveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\\P-P\\), name, \\p-p\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\\P-N\\), name, \\p-n\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\\N-N\\), name, \\n-n\\, outDir)\n\n################################################################################\n# Annotating with one marker (Enhancer)\nname <- \\chromo_cons_annoEnhancer\\\nconsensus.loop.anno.enhancer.tb <- annotateLoopEnhancer(temp.anno.TSS)\nfwrite(consensus.loop.anno.enhancer.tb, here(consensusDir, paste0(name, \\.tsv\\)), \n       sep = \\\\t\\, col.names = TRUE)\ncreateLoopAnnotation(consensus.loop.anno.enhancer.tb, name, \n                     figDir, outDir,  colorListEnhancer)\nsaveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, unique(consensus.loop.anno.enhancer.tb$Anno2) , name, \\all\\, outDir)  \nsaveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\\E-E\\), name, \\e-e\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\\E-N\\), name, \\e-n\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\\N-N\\), name, \\n-n\\, outDir)\n################################################################################\n# Annotating with one marker (Structure)\nname <- \\chromo_cons_annoStructure\\\nconsensus.loop.anno.structure.tb <- annotateLoopStructure(temp.anno.TSS)\nfwrite(consensus.loop.anno.structure.tb, here(consensusDir, paste0(name, \\.tsv\\)), \n       sep = \\\\t\\, col.names = TRUE)\ncreateLoopAnnotation(consensus.loop.anno.structure.tb, name, \n                     figDir, outDir,  colorListStructure)\nsaveAnnoGroupBedpe(consensus.loop.anno.structure.tb, unique(consensus.loop.anno.structure.tb$Anno2) , name, \\all\\, outDir)  \nsaveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\\S-S\\), name, \\s-s\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\\S-N\\), name, \\s-n\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\\N-N\\), name, \\n-n\\, outDir)\n```\n\n<!-- rnb-source-end -->\n"} -->
consensus.loop.tb <- fread(here(consensusDir, \chromo_cons_score.tsv\))
################################################################################
# Filtering H3K4me3 peaks that has TSS nearby
# Since the finest resolution is 5kb, +-2.5kb will be used as a cutoff for checking TSS presence
flankSize <- 2500
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend)
colnames(gene.tb) <- c(\chr\, \start\, \end\)
TSS1kb.gr <- makeGRangesFromDataFrame(gene.tb)
temp <- peak.H3K4me3[unique(queryHits(findOverlaps(peak.H3K4me3, TSS1kb.gr)))]
fwrite(as_tibble(temp), here(refDir,
paste0(\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.\,
flankSize/1000, \kbTSS.bed\)), sep = \\t\, col.names = FALSE)
peak.H3K4me3TSS <- importPeak(here(refDir, paste0(\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.\,
flankSize/1000, \kbTSS.bed\)))
################################################################################
# Annotating with strict priority (P-TSS > E > S)
temp.anno.TSS <- annotateAnchorTSS(consensus.loop.tb) %>%
dplyr::mutate(
diff_dTAG_DMSO = (dTAG-DMSO),
diff_A485_DMSO = (A485-DMSO))
name <- \chromo_cons_annoHierarchy\
consensus.loop.anno.tb <- annotateLoopRelaxedTSS(temp.anno.TSS)
fwrite(consensus.loop.anno.tb, here(consensusDir, paste0(name, \.tsv\)),
sep = \\t\, col.names = TRUE)
createLoopAnnotation(consensus.loop.anno.tb, name, figDir, outDir, colorListLoop)
saveAnnoGroupBedpe(consensus.loop.anno.tb, unique(consensus.loop.anno.tb$Anno2) , name, \all\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\, \P-E\, \P-S\, \P-X\, \E-E\, \E-S\, \E-X\), name, \regulatory\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\S-S\, \S-X\), name, \structure\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\X-X\), name, \x-x\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\, \P-E\, \P-S\, \P-X\), name, \p-n\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\, \P-E\, \E-E\), name, \pe-pe\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\, \P-E\), name, \p-pe\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\), name, \p-p\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-E\), name, \p-e\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-S\), name, \p-s\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-X\), name, \p-x\, outDir)
################################################################################
# Annotating with one marker (Promoter)
name <- \chromo_cons_annoPromoter\
consensus.loop.anno.promoter.tb <- annotateLoopPromoterTSS(temp.anno.TSS)
fwrite(consensus.loop.anno.promoter.tb, here(consensusDir, paste0(name, \.tsv\)),
sep = \\t\, col.names = TRUE)
createLoopAnnotation(consensus.loop.anno.promoter.tb, name,
figDir, outDir, colorListPromoter)
saveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, unique(consensus.loop.anno.promoter.tb$Anno2) , name, \all\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\P-P\), name, \p-p\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\P-N\), name, \p-n\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\N-N\), name, \n-n\, outDir)
################################################################################
# Annotating with one marker (Enhancer)
name <- \chromo_cons_annoEnhancer\
consensus.loop.anno.enhancer.tb <- annotateLoopEnhancer(temp.anno.TSS)
fwrite(consensus.loop.anno.enhancer.tb, here(consensusDir, paste0(name, \.tsv\)),
sep = \\t\, col.names = TRUE)
createLoopAnnotation(consensus.loop.anno.enhancer.tb, name,
figDir, outDir, colorListEnhancer)
saveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, unique(consensus.loop.anno.enhancer.tb$Anno2) , name, \all\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\E-E\), name, \e-e\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\E-N\), name, \e-n\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\N-N\), name, \n-n\, outDir)
################################################################################
# Annotating with one marker (Structure)
name <- \chromo_cons_annoStructure\
consensus.loop.anno.structure.tb <- annotateLoopStructure(temp.anno.TSS)
fwrite(consensus.loop.anno.structure.tb, here(consensusDir, paste0(name, \.tsv\)),
sep = \\t\, col.names = TRUE)
createLoopAnnotation(consensus.loop.anno.structure.tb, name,
figDir, outDir, colorListStructure)
saveAnnoGroupBedpe(consensus.loop.anno.structure.tb, unique(consensus.loop.anno.structure.tb$Anno2) , name, \all\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\S-S\), name, \s-s\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\S-N\), name, \s-n\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\N-N\), name, \n-n\, outDir)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nconsensus.loop.tb <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\n################################################################################\n# Filtering H3K4me3 peaks that has TSS nearby\n# Since the finest resolution is 5kb, +-2.5kb will be used as a cutoff for checking TSS presence\nflankSize <- 2500\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\)\nTSS1kb.gr <- makeGRangesFromDataFrame(gene.tb)\ntemp <- peak.H3K4me3[unique(queryHits(findOverlaps(peak.H3K4me3, TSS1kb.gr)))]\nfwrite(as_tibble(temp), here(refDir, \n                             paste0(\\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.\\, \n                                    flankSize/1000, \\kbTSS.bed\\)), sep = \\\\t\\, col.names = FALSE)\npeak.H3K4me3TSS <- importPeak(here(refDir, paste0(\\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.\\, \n                                    flankSize/1000, \\kbTSS.bed\\)))\n################################################################################\n# Annotating with strict priority (P-TSS > E > S)\ntemp.anno.TSS <- annotateAnchorTSS(consensus.loop.tb) %>%\n  dplyr::mutate(\n    diff_dTAG_DMSO = (dTAG-DMSO),\n    diff_A485_DMSO = (A485-DMSO))\n\nname <- \\chromo_cons_annoHierarchy\\\nconsensus.loop.anno.tb <- annotateLoopRelaxedTSS(temp.anno.TSS)\nfwrite(consensus.loop.anno.tb, here(consensusDir, paste0(name, \\.tsv\\)), \n       sep = \\\\t\\, col.names = TRUE)\ncreateLoopAnnotation(consensus.loop.anno.tb, name, figDir, outDir,  colorListLoop)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, unique(consensus.loop.anno.tb$Anno2) , name, \\all\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\, \\E-E\\, \\E-S\\, \\E-X\\), name, \\regulatory\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\S-S\\, \\S-X\\), name, \\structure\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\X-X\\), name, \\x-x\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\), name, \\p-n\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\, \\P-E\\, \\E-E\\), name, \\pe-pe\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\, \\P-E\\), name, \\p-pe\\, outDir)\n\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-P\\), name, \\p-p\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-E\\), name, \\p-e\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-S\\), name, \\p-s\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.tb, c(\\P-X\\), name, \\p-x\\, outDir)\n\n################################################################################\n# Annotating with one marker (Promoter)\nname <- \\chromo_cons_annoPromoter\\\nconsensus.loop.anno.promoter.tb <- annotateLoopPromoterTSS(temp.anno.TSS)\nfwrite(consensus.loop.anno.promoter.tb, here(consensusDir, paste0(name, \\.tsv\\)), \n       sep = \\\\t\\, col.names = TRUE)\ncreateLoopAnnotation(consensus.loop.anno.promoter.tb, name, \n                     figDir, outDir,  colorListPromoter)\nsaveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, unique(consensus.loop.anno.promoter.tb$Anno2) , name, \\all\\, outDir)  \nsaveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\\P-P\\), name, \\p-p\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\\P-N\\), name, \\p-n\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\\N-N\\), name, \\n-n\\, outDir)\n\n################################################################################\n# Annotating with one marker (Enhancer)\nname <- \\chromo_cons_annoEnhancer\\\nconsensus.loop.anno.enhancer.tb <- annotateLoopEnhancer(temp.anno.TSS)\nfwrite(consensus.loop.anno.enhancer.tb, here(consensusDir, paste0(name, \\.tsv\\)), \n       sep = \\\\t\\, col.names = TRUE)\ncreateLoopAnnotation(consensus.loop.anno.enhancer.tb, name, \n                     figDir, outDir,  colorListEnhancer)\nsaveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, unique(consensus.loop.anno.enhancer.tb$Anno2) , name, \\all\\, outDir)  \nsaveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\\E-E\\), name, \\e-e\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\\E-N\\), name, \\e-n\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\\N-N\\), name, \\n-n\\, outDir)\n################################################################################\n# Annotating with one marker (Structure)\nname <- \\chromo_cons_annoStructure\\\nconsensus.loop.anno.structure.tb <- annotateLoopStructure(temp.anno.TSS)\nfwrite(consensus.loop.anno.structure.tb, here(consensusDir, paste0(name, \\.tsv\\)), \n       sep = \\\\t\\, col.names = TRUE)\ncreateLoopAnnotation(consensus.loop.anno.structure.tb, name, \n                     figDir, outDir,  colorListStructure)\nsaveAnnoGroupBedpe(consensus.loop.anno.structure.tb, unique(consensus.loop.anno.structure.tb$Anno2) , name, \\all\\, outDir)  \nsaveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\\S-S\\), name, \\s-s\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\\S-N\\), name, \\s-n\\, outDir)\nsaveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\\N-N\\), name, \\n-n\\, outDir)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
consensus.loop.tb <- fread(here(consensusDir, \chromo_cons_score.tsv\))
################################################################################
# Filtering H3K4me3 peaks that has TSS nearby
# Since the finest resolution is 5kb, +-2.5kb will be used as a cutoff for checking TSS presence
flankSize <- 2500
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend)
colnames(gene.tb) <- c(\chr\, \start\, \end\)
TSS1kb.gr <- makeGRangesFromDataFrame(gene.tb)
temp <- peak.H3K4me3[unique(queryHits(findOverlaps(peak.H3K4me3, TSS1kb.gr)))]
fwrite(as_tibble(temp), here(refDir,
paste0(\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.\,
flankSize/1000, \kbTSS.bed\)), sep = \\t\, col.names = FALSE)
peak.H3K4me3TSS <- importPeak(here(refDir, paste0(\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.\,
flankSize/1000, \kbTSS.bed\)))
################################################################################
# Annotating with strict priority (P-TSS > E > S)
temp.anno.TSS <- annotateAnchorTSS(consensus.loop.tb) %>%
dplyr::mutate(
diff_dTAG_DMSO = (dTAG-DMSO),
diff_A485_DMSO = (A485-DMSO))
name <- \chromo_cons_annoHierarchy\
consensus.loop.anno.tb <- annotateLoopRelaxedTSS(temp.anno.TSS)
fwrite(consensus.loop.anno.tb, here(consensusDir, paste0(name, \.tsv\)),
sep = \\t\, col.names = TRUE)
createLoopAnnotation(consensus.loop.anno.tb, name, figDir, outDir, colorListLoop)
saveAnnoGroupBedpe(consensus.loop.anno.tb, unique(consensus.loop.anno.tb$Anno2) , name, \all\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\, \P-E\, \P-S\, \P-X\, \E-E\, \E-S\, \E-X\), name, \regulatory\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\S-S\, \S-X\), name, \structure\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\X-X\), name, \x-x\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\, \P-E\, \P-S\, \P-X\), name, \p-n\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\, \P-E\, \E-E\), name, \pe-pe\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\, \P-E\), name, \p-pe\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-P\), name, \p-p\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-E\), name, \p-e\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-S\), name, \p-s\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.tb, c(\P-X\), name, \p-x\, outDir)
################################################################################
# Annotating with one marker (Promoter)
name <- \chromo_cons_annoPromoter\
consensus.loop.anno.promoter.tb <- annotateLoopPromoterTSS(temp.anno.TSS)
fwrite(consensus.loop.anno.promoter.tb, here(consensusDir, paste0(name, \.tsv\)),
sep = \\t\, col.names = TRUE)
createLoopAnnotation(consensus.loop.anno.promoter.tb, name,
figDir, outDir, colorListPromoter)
saveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, unique(consensus.loop.anno.promoter.tb$Anno2) , name, \all\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\P-P\), name, \p-p\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\P-N\), name, \p-n\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.promoter.tb, c(\N-N\), name, \n-n\, outDir)
################################################################################
# Annotating with one marker (Enhancer)
name <- \chromo_cons_annoEnhancer\
consensus.loop.anno.enhancer.tb <- annotateLoopEnhancer(temp.anno.TSS)
fwrite(consensus.loop.anno.enhancer.tb, here(consensusDir, paste0(name, \.tsv\)),
sep = \\t\, col.names = TRUE)
createLoopAnnotation(consensus.loop.anno.enhancer.tb, name,
figDir, outDir, colorListEnhancer)
saveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, unique(consensus.loop.anno.enhancer.tb$Anno2) , name, \all\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\E-E\), name, \e-e\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\E-N\), name, \e-n\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.enhancer.tb, c(\N-N\), name, \n-n\, outDir)
################################################################################
# Annotating with one marker (Structure)
name <- \chromo_cons_annoStructure\
consensus.loop.anno.structure.tb <- annotateLoopStructure(temp.anno.TSS)
fwrite(consensus.loop.anno.structure.tb, here(consensusDir, paste0(name, \.tsv\)),
sep = \\t\, col.names = TRUE)
createLoopAnnotation(consensus.loop.anno.structure.tb, name,
figDir, outDir, colorListStructure)
saveAnnoGroupBedpe(consensus.loop.anno.structure.tb, unique(consensus.loop.anno.structure.tb$Anno2) , name, \all\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\S-S\), name, \s-s\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\S-N\), name, \s-n\, outDir)
saveAnnoGroupBedpe(consensus.loop.anno.structure.tb, c(\N-N\), name, \n-n\, outDir)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Functions
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
################################################################################
# 2024.09.10 Splitting regulatory loop into pure regulatory and structure-related loops
name <- \chromo_cons_annoHierarchy\
consensus.loop.anno.tb <- fread(here(consensusDir, paste0(name, \.tsv\)))
regulatory.loop.anno.tb <- consensus.loop.anno.tb %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
regulatory.loop.anno.tb <- regulatory.loop.anno.tb %>% rowwise() %>%
dplyr::mutate(
Anno3 = ifelse(A1_RAD21 | A1_CTCF | A2_RAD21 | A2_RAD21, paste0(\str\),
paste0(\reg\))
)
fwrite(regulatory.loop.anno.tb, here(consensusDir, paste0(name, \_Anno3.tsv\)),
sep = \\t\, col.names = TRUE)
regulatory.loop.anno.tb_reg <- regulatory.loop.anno.tb %>% dplyr::filter(Anno3 == \reg\)
regulatory.loop.anno.tb_str <- regulatory.loop.anno.tb %>% dplyr::filter(Anno3 == \str\)
saveAnnoGroupBedpe(regulatory.loop.anno.tb_reg, c(\P-P\, \P-E\, \E-E\), name, \pe-pe_reg\, outDir)
saveAnnoGroupBedpe(regulatory.loop.anno.tb_str, c(\P-P\, \P-E\, \E-E\), name, \pe-pe_str\, outDir)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
################################################################################
# 2024.09.10 Splitting regulatory loop into pure regulatory and structure-related loops
name <- \chromo_cons_annoHierarchy\
consensus.loop.anno.tb <- fread(here(consensusDir, paste0(name, \.tsv\)))
regulatory.loop.anno.tb <- consensus.loop.anno.tb %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
regulatory.loop.anno.tb <- regulatory.loop.anno.tb %>% rowwise() %>%
dplyr::mutate(
Anno3 = ifelse(A1_RAD21 | A1_CTCF | A2_RAD21 | A2_RAD21, paste0(\str\),
paste0(\reg\))
)
fwrite(regulatory.loop.anno.tb, here(consensusDir, paste0(name, \_Anno3.tsv\)),
sep = \\t\, col.names = TRUE)
regulatory.loop.anno.tb_reg <- regulatory.loop.anno.tb %>% dplyr::filter(Anno3 == \reg\)
regulatory.loop.anno.tb_str <- regulatory.loop.anno.tb %>% dplyr::filter(Anno3 == \str\)
saveAnnoGroupBedpe(regulatory.loop.anno.tb_reg, c(\P-P\, \P-E\, \E-E\), name, \pe-pe_reg\, outDir)
saveAnnoGroupBedpe(regulatory.loop.anno.tb_str, c(\P-P\, \P-E\, \E-E\), name, \pe-pe_str\, outDir)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Annotation
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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 -->\n\n```r\ncreate_loop_dis_vs_score <- function(data, figDir, name, Anno2List){\n  data = data %>% dplyr::filter(Anno2 %in% Anno2List)\n  \n  ### barplot\n  temp <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%\n    pivot_longer(!id, names_to = \\treatment\\, values_to = \\score\\)\n  temp$treatment <- factor(temp$treatment, levels = c(\\DMSO\\, \\dTAG\\, \\A485\\))\n  \n  p3 <- ggplot(temp, aes(x = treatment, y = score)) +\n    geom_violin(aes(fill = treatment), show.legend = FALSE) +\n    scale_fill_manual(values = c(\\DMSO\\ = \\grey\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    ylim(-0.5, 1) +\n    geom_hline(yintercept = 0, linetype = \\dashed\\, color = \\grey\\) +\n    theme_classic() +\n    ggtitle(name) +\n    theme(plot.title = element_text(size = 4))\n  fileName <- paste0(\\score_barplot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 2, height = 4)\n  print(p3)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 2, height = 4)\n  print(p3)\n  dev.off()\n  \n  ### Distance vs score\n  \n  temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, dTAG, A485)\n\n  avg_scores <- temp %>%\n    group_by(distance) %>%\n    summarise(across(starts_with(\\DMSO\\):starts_with(\\A485\\), mean, na.rm = TRUE))\n\n  avg_scores_long <- avg_scores %>%\n    pivot_longer(cols = DMSO:A485, names_to = \\condition\\, values_to = \\avg_score\\)\n  avg_scores_long$condition <- factor(avg_scores_long$condition, levels = c(\\DMSO\\, \\dTAG\\, \\A485\\))\n  # Create the plot\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +\n    geom_smooth(show.legend = TRUE) + ylim(0, 0.5) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = c(\\DMSO\\ = \\grey\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    scale_fill_manual(values = c(\\DMSO\\ = \\grey80\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Score\\) +\n    theme(plot.title = element_text(size = 5))\n  fileName <- paste0(\\dist_vs_score_linePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)),\n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),\n      width = 4, height = 3)\n  print(p4)\n  dev.off()\n}\n\ncreate_loop_dis_vs_diffscore <- function(data, figDir, name, Anno2List){\n  data = data %>% dplyr::filter(Anno2 %in% Anno2List)\n  \n  \n  temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, diff_A485_DMSO)\n  \n  avg_scores <- temp %>%\n    group_by(distance) %>%\n    summarise(across(1:2, mean, na.rm = TRUE))\n  \n  avg_scores_long <- avg_scores %>%\n    pivot_longer(cols = 2:3, names_to = \\condition\\, values_to = \\avg_score\\)\n  avg_scores_long$condition <- factor(avg_scores_long$condition, \n                                      levels = c(\\diff_dTAG_DMSO\\, \\diff_A485_DMSO\\))\n  # Create the plot\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +\n    geom_smooth(show.legend = TRUE) + geom_hline(yintercept = 0) + ylim(-0.5, 0.1) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = c(\\diff_dTAG_DMSO\\ = \\pink\\, \\diff_A485_DMSO\\ = \\skyblue\\)) +\n    scale_fill_manual(values = c(\\diff_dTAG_DMSO\\ = \\pink\\, \\diff_A485_DMSO\\ = \\skyblue\\)) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Diff Score\\) +\n    theme(plot.title = element_text(size = 5))\n  fileName <- paste0(\\dist_vs_score_difflinePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)),\n      res = 600, units = \\in\\, width = 4.5, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),\n          width = 4.5, height = 3)\n  print(p4)\n  dev.off()\n}\n\ncreate_loop_scatterplot <- function(data, figDir, name, Anno2List, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  num.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\n  num.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\n  num.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\n  num.all <- num.up + num.no + num.down\n  perc.up <- round(num.up / num.all * 100, 2)\n  perc.no <- round(num.no / num.all * 100, 2)\n  perc.down <- round(num.down / num.all * 100, 2)\n  \n  ### Scatterplot\n  data$density <- get_density(data$DMSO, data$dTAG, n = 100)\n  data <- data %>% dplyr::arrange(density)\n  correlation <- cor(data$DMSO, data$dTAG)\n  p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n    geom_point(size = 1,\n      alpha = 1,\n      stroke = 0) + \n    scale_color_viridis(option = \\D\\, guide = guide_colorbar(\n                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n                          barheight = 15/5.08   # Adjust height of the color bar\n                        )) +\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    theme_classic() +\n    #annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\) +\n    theme(\n      # legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n  xlab(\\Loop score\\nG1.DMSO\\) +\n    ylab(\\Loop score\\nG1.dTAG\\)\n  \n  \n  \n  num.up <- (summary(data$updown_A485_DMSO))[\\UP\\]\n  num.no <- (summary(data$updown_A485_DMSO))[\\NO\\]\n  num.down <- (summary(data$updown_A485_DMSO))[\\DOWN\\]\n  num.all <- num.up + num.no + num.down\n  perc.up <- round(num.up / num.all * 100, 2)\n  perc.no <- round(num.no / num.all * 100, 2)\n  perc.down <- round(num.down / num.all * 100, 2)\n  \n  data$density <- get_density(data$DMSO, data$A485, n = 100)\n  data <- data %>% dplyr::arrange(density)\n  correlation <- cor(data$DMSO, data$A485)\n  p2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +\n    geom_point(size = 1,\n      alpha = 1,\n      stroke = 0) + \n    scale_color_viridis(option = \\D\\, guide = guide_colorbar(\n                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n                          barheight = 15/5.08   # Adjust height of the color bar\n                        )) +\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    theme_classic() + \n    # annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\) +\n    theme(\n      # legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n    xlab(\\Loop score\\nG1.DMSO\\) +\n    ylab(\\Loop score\\nG1.A485\\)\n\n    width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch  \n  \n  fileName <- here(figDir,paste0(\\scatterplot_\\, name, \\_dTAG_vs_DMSO_\\, diffCutoff))\n\n  svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n  print(p1)\n  dev.off()\n  png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n  print(p1)\n  dev.off()\n  \n    fileName <- here(figDir, paste0(\\scatterplot_\\, name, \\_A485_vs_DMSO_\\, diffCutoff))\n\n  svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n  print(p2)\n  dev.off()\n  png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n  print(p2)\n  dev.off()\n  \n  # \n  # \n  # \n  # png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 5*1.5, height = 2.5*1.5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n  # \n  # svglite(here(figDir, paste0(fileName, \\.svg\\)), \n  #         width = 5*1.5, height = 2.5*1.5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n}\n\nmake_diff_bedpe <- function(data, name, Anno2List, outDir, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_UP_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_NO_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_DOWN_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  \n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\UP\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_UP_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\NO\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_NO_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\DOWN\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_DOWN_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n}\n\ncreate_dist_vs_avgScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_smooth(show.legend = TRUE, se = se)  +\n    ylim(0, 0.5) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Score\\) +\n    theme(plot.title = element_text(size = 8))\n  \n  fileName <- paste0(\\dist_vs_score_linePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_dist_vs_avgDiffScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_hline(yintercept = 0) +\n    geom_smooth(show.legend = TRUE, se = se)  +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Diff Score\\) +\n    theme(plot.title = element_text(size = 8)) \n  \n  fileName <- paste0(\\dist_vs_score_difflinePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_score_barplot_perTreatment <- function(data, figDir, name, loopList, colorList){\n  data$Anno2 <- factor(data$Anno2, levels = loopList)\n  \n  p3 <- ggplot(data, aes(x = Anno2, y = score)) +\n    geom_violin(aes(fill = Anno2), show.legend = FALSE) +\n    scale_fill_manual(values = colorList) +\n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    ylim(-0.5, 1) +\n    geom_hline(yintercept = 0, linetype = \\dashed\\, color = \\grey\\) +\n    theme_classic() +\n    ggtitle(name) +\n    theme(plot.title = element_text(size = 4))\n  \n  fileName <- paste0(\\score_barPlot_\\, name)\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 2, height = 4)\n  print(p3)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 2, height = 4)\n  print(p3)\n}\n\n\niterate_loop_functions <- function(data, figDir, groupName, annoList){\n  # create_loop_dis_vs_score(data, figDir, groupName, annoList)\n  # create_loop_dis_vs_diffscore(data, figDir, groupName, annoList)\n\n  create_loop_scatterplot(data, figDir, groupName, annoList, 0.2)\n  # create_loop_scatterplot(data, figDir, groupName, annoList, 0.1)\n  # make_diff_bedpe(data, groupName, annoList, consensusDir, 0.2)\n  # make_diff_bedpe(data, groupName, annoList, consensusDir, 0.1)\n}\n```\n\n<!-- rnb-source-end -->\n"} -->
create_loop_dis_vs_score <- function(data, figDir, name, Anno2List){
data = data %>% dplyr::filter(Anno2 %in% Anno2List)
### barplot
temp <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%
pivot_longer(!id, names_to = \treatment\, values_to = \score\)
temp$treatment <- factor(temp$treatment, levels = c(\DMSO\, \dTAG\, \A485\))
p3 <- ggplot(temp, aes(x = treatment, y = score)) +
geom_violin(aes(fill = treatment), show.legend = FALSE) +
scale_fill_manual(values = c(\DMSO\ = \grey\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
ylim(-0.5, 1) +
geom_hline(yintercept = 0, linetype = \dashed\, color = \grey\) +
theme_classic() +
ggtitle(name) +
theme(plot.title = element_text(size = 4))
fileName <- paste0(\score_barplot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 2, height = 4)
print(p3)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 2, height = 4)
print(p3)
dev.off()
### Distance vs score
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, dTAG, A485)
avg_scores <- temp %>%
group_by(distance) %>%
summarise(across(starts_with(\DMSO\):starts_with(\A485\), mean, na.rm = TRUE))
avg_scores_long <- avg_scores %>%
pivot_longer(cols = DMSO:A485, names_to = \condition\, values_to = \avg_score\)
avg_scores_long$condition <- factor(avg_scores_long$condition, levels = c(\DMSO\, \dTAG\, \A485\))
# Create the plot
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +
geom_smooth(show.legend = TRUE) + ylim(0, 0.5) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = c(\DMSO\ = \grey\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
scale_fill_manual(values = c(\DMSO\ = \grey80\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Score\) +
theme(plot.title = element_text(size = 5))
fileName <- paste0(\dist_vs_score_linePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_loop_dis_vs_diffscore <- function(data, figDir, name, Anno2List){
data = data %>% dplyr::filter(Anno2 %in% Anno2List)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, diff_A485_DMSO)
avg_scores <- temp %>%
group_by(distance) %>%
summarise(across(1:2, mean, na.rm = TRUE))
avg_scores_long <- avg_scores %>%
pivot_longer(cols = 2:3, names_to = \condition\, values_to = \avg_score\)
avg_scores_long$condition <- factor(avg_scores_long$condition,
levels = c(\diff_dTAG_DMSO\, \diff_A485_DMSO\))
# Create the plot
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +
geom_smooth(show.legend = TRUE) + geom_hline(yintercept = 0) + ylim(-0.5, 0.1) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = c(\diff_dTAG_DMSO\ = \pink\, \diff_A485_DMSO\ = \skyblue\)) +
scale_fill_manual(values = c(\diff_dTAG_DMSO\ = \pink\, \diff_A485_DMSO\ = \skyblue\)) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Diff Score\) +
theme(plot.title = element_text(size = 5))
fileName <- paste0(\dist_vs_score_difflinePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4.5, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4.5, height = 3)
print(p4)
dev.off()
}
create_loop_scatterplot <- function(data, figDir, name, Anno2List, diffCutoff){
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% Anno2List)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
### Scatterplot
data$density <- get_density(data$DMSO, data$dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
correlation <- cor(data$DMSO, data$dTAG)
p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +
geom_point(size = 1,
alpha = 1,
stroke = 0) +
scale_color_viridis(option = \D\, guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)) +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, , size = 1,
family = fontType) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, , size = 1,
family = fontType) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, , size = 1,
family = fontType) +
theme_classic() +
#annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\) +
theme(
# legend.position = \none\,
plot.title = element_text(
hjust = 0.5,
size = fontSizeS,
family = fontType
),
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
) +
xlab(\Loop score\nG1.DMSO\) +
ylab(\Loop score\nG1.dTAG\)
num.up <- (summary(data$updown_A485_DMSO))[\UP\]
num.no <- (summary(data$updown_A485_DMSO))[\NO\]
num.down <- (summary(data$updown_A485_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$A485, n = 100)
data <- data %>% dplyr::arrange(density)
correlation <- cor(data$DMSO, data$A485)
p2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +
geom_point(size = 1,
alpha = 1,
stroke = 0) +
scale_color_viridis(option = \D\, guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)) +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 1,
family = fontType) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 1,
family = fontType) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 1,
family = fontType) +
theme_classic() +
# annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\) +
theme(
# legend.position = \none\,
plot.title = element_text(
hjust = 0.5,
size = fontSizeS,
family = fontType
),
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
) +
xlab(\Loop score\nG1.DMSO\) +
ylab(\Loop score\nG1.A485\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- here(figDir,paste0(\scatterplot_\, name, \_dTAG_vs_DMSO_\, diffCutoff))
svglite(paste0(fileName, \.svg\), width = width, height =height)
print(p1)
dev.off()
png(paste0(fileName, \.png\), width = width, height =height, res = 600, unit = \in\)
print(p1)
dev.off()
fileName <- here(figDir, paste0(\scatterplot_\, name, \_A485_vs_DMSO_\, diffCutoff))
svglite(paste0(fileName, \.svg\), width = width, height =height)
print(p2)
dev.off()
png(paste0(fileName, \.png\), width = width, height =height, res = 600, unit = \in\)
print(p2)
dev.off()
#
#
#
# png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 5*1.5, height = 2.5*1.5)
# print(cowplot::plot_grid(p1, p2, align = \h\))
# dev.off()
#
# svglite(here(figDir, paste0(fileName, \.svg\)),
# width = 5*1.5, height = 2.5*1.5)
# print(cowplot::plot_grid(p1, p2, align = \h\))
# dev.off()
}
make_diff_bedpe <- function(data, name, Anno2List, outDir, diffCutoff){
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% Anno2List)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \UP\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_UP_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \NO\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_NO_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \DOWN\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_DOWN_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \UP\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_UP_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \NO\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_NO_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \DOWN\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_DOWN_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
}
create_dist_vs_avgScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){
avg_scores_long <- data %>%
group_by(distance, Anno2) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) +
geom_smooth(show.legend = TRUE, se = se) +
ylim(0, 0.5) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Score\) +
theme(plot.title = element_text(size = 8))
fileName <- paste0(\dist_vs_score_linePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_dist_vs_avgDiffScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){
avg_scores_long <- data %>%
group_by(distance, Anno2) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) +
geom_hline(yintercept = 0) +
geom_smooth(show.legend = TRUE, se = se) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Diff Score\) +
theme(plot.title = element_text(size = 8))
fileName <- paste0(\dist_vs_score_difflinePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_score_barplot_perTreatment <- function(data, figDir, name, loopList, colorList){
data$Anno2 <- factor(data$Anno2, levels = loopList)
p3 <- ggplot(data, aes(x = Anno2, y = score)) +
geom_violin(aes(fill = Anno2), show.legend = FALSE) +
scale_fill_manual(values = colorList) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
ylim(-0.5, 1) +
geom_hline(yintercept = 0, linetype = \dashed\, color = \grey\) +
theme_classic() +
ggtitle(name) +
theme(plot.title = element_text(size = 4))
fileName <- paste0(\score_barPlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 2, height = 4)
print(p3)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 2, height = 4)
print(p3)
}
iterate_loop_functions <- function(data, figDir, groupName, annoList){
# create_loop_dis_vs_score(data, figDir, groupName, annoList)
# create_loop_dis_vs_diffscore(data, figDir, groupName, annoList)
create_loop_scatterplot(data, figDir, groupName, annoList, 0.2)
# create_loop_scatterplot(data, figDir, groupName, annoList, 0.1)
# make_diff_bedpe(data, groupName, annoList, consensusDir, 0.2)
# make_diff_bedpe(data, groupName, annoList, consensusDir, 0.1)
}
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n    xlab(\\Loop score\\nG1.DMSO\\) +\n    ylab(\\Loop score\\nG1.A485\\)\n\n    width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch  \n  \n  fileName <- here(figDir,paste0(\\scatterplot_\\, name, \\_dTAG_vs_DMSO_\\, diffCutoff))\n\n  svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n  print(p1)\n  dev.off()\n  png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n  print(p1)\n  dev.off()\n  \n    fileName <- here(figDir, paste0(\\scatterplot_\\, name, \\_A485_vs_DMSO_\\, diffCutoff))\n\n  svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n  print(p2)\n  dev.off()\n  png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n  print(p2)\n  dev.off()\n  \n  # \n  # \n  # \n  # png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 5*1.5, height = 2.5*1.5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n  # \n  # svglite(here(figDir, paste0(fileName, \\.svg\\)), \n  #         width = 5*1.5, height = 2.5*1.5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n}\n\nmake_diff_bedpe <- function(data, name, Anno2List, outDir, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_UP_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_NO_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_DOWN_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  \n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\UP\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_UP_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\NO\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_NO_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\DOWN\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_DOWN_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n}\n\ncreate_dist_vs_avgScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_smooth(show.legend = TRUE, se = se)  +\n    ylim(0, 0.5) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Score\\) +\n    theme(plot.title = element_text(size = 8))\n  \n  fileName <- paste0(\\dist_vs_score_linePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_dist_vs_avgDiffScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_hline(yintercept = 0) +\n    geom_smooth(show.legend = TRUE, se = se)  +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Diff Score\\) +\n    theme(plot.title = element_text(size = 8)) \n  \n  fileName <- paste0(\\dist_vs_score_difflinePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_score_barplot_perTreatment <- function(data, figDir, name, loopList, colorList){\n  data$Anno2 <- factor(data$Anno2, levels = loopList)\n  \n  p3 <- ggplot(data, aes(x = Anno2, y = score)) +\n    geom_violin(aes(fill = Anno2), show.legend = FALSE) +\n    scale_fill_manual(values = colorList) +\n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    ylim(-0.5, 1) +\n    geom_hline(yintercept = 0, linetype = \\dashed\\, color = \\grey\\) +\n    theme_classic() +\n    ggtitle(name) +\n    theme(plot.title = element_text(size = 4))\n  \n  fileName <- paste0(\\score_barPlot_\\, name)\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 2, height = 4)\n  print(p3)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 2, height = 4)\n  print(p3)\n}\n\n\niterate_loop_functions <- function(data, figDir, groupName, annoList){\n  # create_loop_dis_vs_score(data, figDir, groupName, annoList)\n  # create_loop_dis_vs_diffscore(data, figDir, groupName, annoList)\n\n  create_loop_scatterplot(data, figDir, groupName, annoList, 0.2)\n  # create_loop_scatterplot(data, figDir, groupName, annoList, 0.1)\n  # make_diff_bedpe(data, groupName, annoList, consensusDir, 0.2)\n  # make_diff_bedpe(data, groupName, annoList, consensusDir, 0.1)\n}\n```\n```"} -->\n\n```r\n```r\ncreate_loop_dis_vs_score <- function(data, figDir, name, Anno2List){\n  data = data %>% dplyr::filter(Anno2 %in% Anno2List)\n  \n  ### barplot\n  temp <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%\n    pivot_longer(!id, names_to = \\treatment\\, values_to = \\score\\)\n  temp$treatment <- factor(temp$treatment, levels = c(\\DMSO\\, \\dTAG\\, \\A485\\))\n  \n  p3 <- ggplot(temp, aes(x = treatment, y = score)) +\n    geom_violin(aes(fill = treatment), show.legend = FALSE) +\n    scale_fill_manual(values = c(\\DMSO\\ = \\grey\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    ylim(-0.5, 1) +\n    geom_hline(yintercept = 0, linetype = \\dashed\\, color = \\grey\\) +\n    theme_classic() +\n    ggtitle(name) +\n    theme(plot.title = element_text(size = 4))\n  fileName <- paste0(\\score_barplot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 2, height = 4)\n  print(p3)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 2, height = 4)\n  print(p3)\n  dev.off()\n  \n  ### Distance vs score\n  \n  temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, dTAG, A485)\n\n  avg_scores <- temp %>%\n    group_by(distance) %>%\n    summarise(across(starts_with(\\DMSO\\):starts_with(\\A485\\), mean, na.rm = TRUE))\n\n  avg_scores_long <- avg_scores %>%\n    pivot_longer(cols = DMSO:A485, names_to = \\condition\\, values_to = \\avg_score\\)\n  avg_scores_long$condition <- factor(avg_scores_long$condition, levels = c(\\DMSO\\, \\dTAG\\, \\A485\\))\n  # Create the plot\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +\n    geom_smooth(show.legend = TRUE) + ylim(0, 0.5) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = c(\\DMSO\\ = \\grey\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    scale_fill_manual(values = c(\\DMSO\\ = \\grey80\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Score\\) +\n    theme(plot.title = element_text(size = 5))\n  fileName <- paste0(\\dist_vs_score_linePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)),\n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),\n      width = 4, height = 3)\n  print(p4)\n  dev.off()\n}\n\ncreate_loop_dis_vs_diffscore <- function(data, figDir, name, Anno2List){\n  data = data %>% dplyr::filter(Anno2 %in% Anno2List)\n  \n  \n  temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, diff_A485_DMSO)\n  \n  avg_scores <- temp %>%\n    group_by(distance) %>%\n    summarise(across(1:2, mean, na.rm = TRUE))\n  \n  avg_scores_long <- avg_scores %>%\n    pivot_longer(cols = 2:3, names_to = \\condition\\, values_to = \\avg_score\\)\n  avg_scores_long$condition <- factor(avg_scores_long$condition, \n                                      levels = c(\\diff_dTAG_DMSO\\, \\diff_A485_DMSO\\))\n  # Create the plot\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +\n    geom_smooth(show.legend = TRUE) + geom_hline(yintercept = 0) + ylim(-0.5, 0.1) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = c(\\diff_dTAG_DMSO\\ = \\pink\\, \\diff_A485_DMSO\\ = \\skyblue\\)) +\n    scale_fill_manual(values = c(\\diff_dTAG_DMSO\\ = \\pink\\, \\diff_A485_DMSO\\ = \\skyblue\\)) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Diff Score\\) +\n    theme(plot.title = element_text(size = 5))\n  fileName <- paste0(\\dist_vs_score_difflinePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)),\n      res = 600, units = \\in\\, width = 4.5, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),\n          width = 4.5, height = 3)\n  print(p4)\n  dev.off()\n}\n\ncreate_loop_scatterplot <- function(data, figDir, name, Anno2List, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  num.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\n  num.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\n  num.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\n  num.all <- num.up + num.no + num.down\n  perc.up <- round(num.up / num.all * 100, 2)\n  perc.no <- round(num.no / num.all * 100, 2)\n  perc.down <- round(num.down / num.all * 100, 2)\n  \n  ### Scatterplot\n  data$density <- get_density(data$DMSO, data$dTAG, n = 100)\n  data <- data %>% dplyr::arrange(density)\n  correlation <- cor(data$DMSO, data$dTAG)\n  p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n    geom_point(size = 1,\n      alpha = 1,\n      stroke = 0) + \n    scale_color_viridis(option = \\D\\, guide = guide_colorbar(\n                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n                          barheight = 15/5.08   # Adjust height of the color bar\n                        )) +\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    theme_classic() +\n    #annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\) +\n    theme(\n      # legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n  xlab(\\Loop score\\nG1.DMSO\\) +\n    ylab(\\Loop score\\nG1.dTAG\\)\n  \n  \n  \n  num.up <- (summary(data$updown_A485_DMSO))[\\UP\\]\n  num.no <- (summary(data$updown_A485_DMSO))[\\NO\\]\n  num.down <- (summary(data$updown_A485_DMSO))[\\DOWN\\]\n  num.all <- num.up + num.no + num.down\n  perc.up <- round(num.up / num.all * 100, 2)\n  perc.no <- round(num.no / num.all * 100, 2)\n  perc.down <- round(num.down / num.all * 100, 2)\n  \n  data$density <- get_density(data$DMSO, data$A485, n = 100)\n  data <- data %>% dplyr::arrange(density)\n  correlation <- cor(data$DMSO, data$A485)\n  p2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +\n    geom_point(size = 1,\n      alpha = 1,\n      stroke = 0) + \n    scale_color_viridis(option = \\D\\, guide = guide_colorbar(\n                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n                          barheight = 15/5.08   # Adjust height of the color bar\n                        )) +\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    theme_classic() + \n    # annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\) +\n    theme(\n      # legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n    xlab(\\Loop score\\nG1.DMSO\\) +\n    ylab(\\Loop score\\nG1.A485\\)\n\n    width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch  \n  \n  fileName <- here(figDir,paste0(\\scatterplot_\\, name, \\_dTAG_vs_DMSO_\\, diffCutoff))\n\n  svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n  print(p1)\n  dev.off()\n  png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n  print(p1)\n  dev.off()\n  \n    fileName <- here(figDir, paste0(\\scatterplot_\\, name, \\_A485_vs_DMSO_\\, diffCutoff))\n\n  svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n  print(p2)\n  dev.off()\n  png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n  print(p2)\n  dev.off()\n  \n  # \n  # \n  # \n  # png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 5*1.5, height = 2.5*1.5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n  # \n  # svglite(here(figDir, paste0(fileName, \\.svg\\)), \n  #         width = 5*1.5, height = 2.5*1.5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n}\n\nmake_diff_bedpe <- function(data, name, Anno2List, outDir, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_UP_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_NO_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_DOWN_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  \n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\UP\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_UP_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\NO\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_NO_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\DOWN\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_DOWN_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n}\n\ncreate_dist_vs_avgScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_smooth(show.legend = TRUE, se = se)  +\n    ylim(0, 0.5) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Score\\) +\n    theme(plot.title = element_text(size = 8))\n  \n  fileName <- paste0(\\dist_vs_score_linePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_dist_vs_avgDiffScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_hline(yintercept = 0) +\n    geom_smooth(show.legend = TRUE, se = se)  +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Diff Score\\) +\n    theme(plot.title = element_text(size = 8)) \n  \n  fileName <- paste0(\\dist_vs_score_difflinePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_score_barplot_perTreatment <- function(data, figDir, name, loopList, colorList){\n  data$Anno2 <- factor(data$Anno2, levels = loopList)\n  \n  p3 <- ggplot(data, aes(x = Anno2, y = score)) +\n    geom_violin(aes(fill = Anno2), show.legend = FALSE) +\n    scale_fill_manual(values = colorList) +\n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    ylim(-0.5, 1) +\n    geom_hline(yintercept = 0, linetype = \\dashed\\, color = \\grey\\) +\n    theme_classic() +\n    ggtitle(name) +\n    theme(plot.title = element_text(size = 4))\n  \n  fileName <- paste0(\\score_barPlot_\\, name)\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 2, height = 4)\n  print(p3)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 2, height = 4)\n  print(p3)\n}\n\n\niterate_loop_functions <- function(data, figDir, groupName, annoList){\n  # create_loop_dis_vs_score(data, figDir, groupName, annoList)\n  # create_loop_dis_vs_diffscore(data, figDir, groupName, annoList)\n\n  create_loop_scatterplot(data, figDir, groupName, annoList, 0.2)\n  # create_loop_scatterplot(data, figDir, groupName, annoList, 0.1)\n  # make_diff_bedpe(data, groupName, annoList, consensusDir, 0.2)\n  # make_diff_bedpe(data, groupName, annoList, consensusDir, 0.1)\n}\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ncreate_loop_dis_vs_score <- function(data, figDir, name, Anno2List){\n  data = data %>% dplyr::filter(Anno2 %in% Anno2List)\n  \n  ### barplot\n  temp <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%\n    pivot_longer(!id, names_to = \\treatment\\, values_to = \\score\\)\n  temp$treatment <- factor(temp$treatment, levels = c(\\DMSO\\, \\dTAG\\, \\A485\\))\n  \n  p3 <- ggplot(temp, aes(x = treatment, y = score)) +\n    geom_violin(aes(fill = treatment), show.legend = FALSE) +\n    scale_fill_manual(values = c(\\DMSO\\ = \\grey\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    ylim(-0.5, 1) +\n    geom_hline(yintercept = 0, linetype = \\dashed\\, color = \\grey\\) +\n    theme_classic() +\n    ggtitle(name) +\n    theme(plot.title = element_text(size = 4))\n  fileName <- paste0(\\score_barplot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 2, height = 4)\n  print(p3)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 2, height = 4)\n  print(p3)\n  dev.off()\n  \n  ### Distance vs score\n  \n  temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, dTAG, A485)\n\n  avg_scores <- temp %>%\n    group_by(distance) %>%\n    summarise(across(starts_with(\\DMSO\\):starts_with(\\A485\\), mean, na.rm = TRUE))\n\n  avg_scores_long <- avg_scores %>%\n    pivot_longer(cols = DMSO:A485, names_to = \\condition\\, values_to = \\avg_score\\)\n  avg_scores_long$condition <- factor(avg_scores_long$condition, levels = c(\\DMSO\\, \\dTAG\\, \\A485\\))\n  # Create the plot\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +\n    geom_smooth(show.legend = TRUE) + ylim(0, 0.5) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = c(\\DMSO\\ = \\grey\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    scale_fill_manual(values = c(\\DMSO\\ = \\grey80\\, \\dTAG\\ = \\pink\\, \\A485\\ = \\skyblue\\)) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Score\\) +\n    theme(plot.title = element_text(size = 5))\n  fileName <- paste0(\\dist_vs_score_linePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)),\n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),\n      width = 4, height = 3)\n  print(p4)\n  dev.off()\n}\n\ncreate_loop_dis_vs_diffscore <- function(data, figDir, name, Anno2List){\n  data = data %>% dplyr::filter(Anno2 %in% Anno2List)\n  \n  \n  temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, diff_A485_DMSO)\n  \n  avg_scores <- temp %>%\n    group_by(distance) %>%\n    summarise(across(1:2, mean, na.rm = TRUE))\n  \n  avg_scores_long <- avg_scores %>%\n    pivot_longer(cols = 2:3, names_to = \\condition\\, values_to = \\avg_score\\)\n  avg_scores_long$condition <- factor(avg_scores_long$condition, \n                                      levels = c(\\diff_dTAG_DMSO\\, \\diff_A485_DMSO\\))\n  # Create the plot\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +\n    geom_smooth(show.legend = TRUE) + geom_hline(yintercept = 0) + ylim(-0.5, 0.1) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = c(\\diff_dTAG_DMSO\\ = \\pink\\, \\diff_A485_DMSO\\ = \\skyblue\\)) +\n    scale_fill_manual(values = c(\\diff_dTAG_DMSO\\ = \\pink\\, \\diff_A485_DMSO\\ = \\skyblue\\)) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Diff Score\\) +\n    theme(plot.title = element_text(size = 5))\n  fileName <- paste0(\\dist_vs_score_difflinePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)),\n      res = 600, units = \\in\\, width = 4.5, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),\n          width = 4.5, height = 3)\n  print(p4)\n  dev.off()\n}\n\ncreate_loop_scatterplot <- function(data, figDir, name, Anno2List, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  num.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\n  num.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\n  num.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\n  num.all <- num.up + num.no + num.down\n  perc.up <- round(num.up / num.all * 100, 2)\n  perc.no <- round(num.no / num.all * 100, 2)\n  perc.down <- round(num.down / num.all * 100, 2)\n  \n  ### Scatterplot\n  data$density <- get_density(data$DMSO, data$dTAG, n = 100)\n  data <- data %>% dplyr::arrange(density)\n  correlation <- cor(data$DMSO, data$dTAG)\n  p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n    geom_point(size = 1,\n      alpha = 1,\n      stroke = 0) + \n    scale_color_viridis(option = \\D\\, guide = guide_colorbar(\n                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n                          barheight = 15/5.08   # Adjust height of the color bar\n                        )) +\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n             color = \\black\\, hjust = 0, , size = 1,\n        family = fontType) +\n    theme_classic() +\n    #annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\) +\n    theme(\n      # legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n  xlab(\\Loop score\\nG1.DMSO\\) +\n    ylab(\\Loop score\\nG1.dTAG\\)\n  \n  \n  \n  num.up <- (summary(data$updown_A485_DMSO))[\\UP\\]\n  num.no <- (summary(data$updown_A485_DMSO))[\\NO\\]\n  num.down <- (summary(data$updown_A485_DMSO))[\\DOWN\\]\n  num.all <- num.up + num.no + num.down\n  perc.up <- round(num.up / num.all * 100, 2)\n  perc.no <- round(num.no / num.all * 100, 2)\n  perc.down <- round(num.down / num.all * 100, 2)\n  \n  data$density <- get_density(data$DMSO, data$A485, n = 100)\n  data <- data %>% dplyr::arrange(density)\n  correlation <- cor(data$DMSO, data$A485)\n  p2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +\n    geom_point(size = 1,\n      alpha = 1,\n      stroke = 0) + \n    scale_color_viridis(option = \\D\\, guide = guide_colorbar(\n                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n                          barheight = 15/5.08   # Adjust height of the color bar\n                        )) +\n    xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\,\n      color = \\black\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\) +\n    annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n             color = \\black\\, hjust = 0, size = 1,\n        family = fontType) +\n    theme_classic() + \n    # annotate(\\text\\, x = -0.5, y = 1, label = paste(\\r =\\, round(correlation, 2)), size = 5, color = \\black\\) +\n    theme(\n      # legend.position = \\none\\,\n      plot.title = element_text(\n        hjust = 0.5,\n        size = fontSizeS,\n        family = fontType\n      ),\n      axis.title = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS,\n        family = fontType,\n        color = \\#000000\\\n      ),\n      axis.line = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\,\n        size = lineThick*mmToLineUnit,\n        lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n    ) +\n    xlab(\\Loop score\\nG1.DMSO\\) +\n    ylab(\\Loop score\\nG1.A485\\)\n\n    width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.5)*mmToInch  \n  \n  fileName <- here(figDir,paste0(\\scatterplot_\\, name, \\_dTAG_vs_DMSO_\\, diffCutoff))\n\n  svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n  print(p1)\n  dev.off()\n  png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n  print(p1)\n  dev.off()\n  \n    fileName <- here(figDir, paste0(\\scatterplot_\\, name, \\_A485_vs_DMSO_\\, diffCutoff))\n\n  svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n  print(p2)\n  dev.off()\n  png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n  print(p2)\n  dev.off()\n  \n  # \n  # \n  # \n  # png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 5*1.5, height = 2.5*1.5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n  # \n  # svglite(here(figDir, paste0(fileName, \\.svg\\)), \n  #         width = 5*1.5, height = 2.5*1.5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n}\n\nmake_diff_bedpe <- function(data, name, Anno2List, outDir, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n  \n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_UP_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_NO_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_dTAGvsDMSO_DOWN_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  \n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\UP\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_UP_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\NO\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_NO_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \\DOWN\\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \\_A485vsDMSO_DOWN_diff\\, diffCutoff, \\.bedpe\\)), \n         sep = \\\\t\\, col.names = FALSE)\n}\n\ncreate_dist_vs_avgScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_smooth(show.legend = TRUE, se = se)  +\n    ylim(0, 0.5) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Score\\) +\n    theme(plot.title = element_text(size = 8))\n  \n  fileName <- paste0(\\dist_vs_score_linePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_dist_vs_avgDiffScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_hline(yintercept = 0) +\n    geom_smooth(show.legend = TRUE, se = se)  +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \\Distance\\,\n         y = \\Average Diff Score\\) +\n    theme(plot.title = element_text(size = 8)) \n  \n  fileName <- paste0(\\dist_vs_score_difflinePlot_\\, name)\n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_score_barplot_perTreatment <- function(data, figDir, name, loopList, colorList){\n  data$Anno2 <- factor(data$Anno2, levels = loopList)\n  \n  p3 <- ggplot(data, aes(x = Anno2, y = score)) +\n    geom_violin(aes(fill = Anno2), show.legend = FALSE) +\n    scale_fill_manual(values = colorList) +\n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    ylim(-0.5, 1) +\n    geom_hline(yintercept = 0, linetype = \\dashed\\, color = \\grey\\) +\n    theme_classic() +\n    ggtitle(name) +\n    theme(plot.title = element_text(size = 4))\n  \n  fileName <- paste0(\\score_barPlot_\\, name)\n  \n  png(here(figDir, paste0(fileName, \\.png\\)), \n      res = 600, units = \\in\\, width = 2, height = 4)\n  print(p3)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 2, height = 4)\n  print(p3)\n}\n\n\niterate_loop_functions <- function(data, figDir, groupName, annoList){\n  # create_loop_dis_vs_score(data, figDir, groupName, annoList)\n  # create_loop_dis_vs_diffscore(data, figDir, groupName, annoList)\n\n  create_loop_scatterplot(data, figDir, groupName, annoList, 0.2)\n  # create_loop_scatterplot(data, figDir, groupName, annoList, 0.1)\n  # make_diff_bedpe(data, groupName, annoList, consensusDir, 0.2)\n  # make_diff_bedpe(data, groupName, annoList, consensusDir, 0.1)\n}\n```\n```"} -->
```r
```r
create_loop_dis_vs_score <- function(data, figDir, name, Anno2List){
data = data %>% dplyr::filter(Anno2 %in% Anno2List)
### barplot
temp <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%
pivot_longer(!id, names_to = \treatment\, values_to = \score\)
temp$treatment <- factor(temp$treatment, levels = c(\DMSO\, \dTAG\, \A485\))
p3 <- ggplot(temp, aes(x = treatment, y = score)) +
geom_violin(aes(fill = treatment), show.legend = FALSE) +
scale_fill_manual(values = c(\DMSO\ = \grey\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
ylim(-0.5, 1) +
geom_hline(yintercept = 0, linetype = \dashed\, color = \grey\) +
theme_classic() +
ggtitle(name) +
theme(plot.title = element_text(size = 4))
fileName <- paste0(\score_barplot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 2, height = 4)
print(p3)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 2, height = 4)
print(p3)
dev.off()
### Distance vs score
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, dTAG, A485)
avg_scores <- temp %>%
group_by(distance) %>%
summarise(across(starts_with(\DMSO\):starts_with(\A485\), mean, na.rm = TRUE))
avg_scores_long <- avg_scores %>%
pivot_longer(cols = DMSO:A485, names_to = \condition\, values_to = \avg_score\)
avg_scores_long$condition <- factor(avg_scores_long$condition, levels = c(\DMSO\, \dTAG\, \A485\))
# Create the plot
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +
geom_smooth(show.legend = TRUE) + ylim(0, 0.5) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = c(\DMSO\ = \grey\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
scale_fill_manual(values = c(\DMSO\ = \grey80\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Score\) +
theme(plot.title = element_text(size = 5))
fileName <- paste0(\dist_vs_score_linePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_loop_dis_vs_diffscore <- function(data, figDir, name, Anno2List){
data = data %>% dplyr::filter(Anno2 %in% Anno2List)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, diff_A485_DMSO)
avg_scores <- temp %>%
group_by(distance) %>%
summarise(across(1:2, mean, na.rm = TRUE))
avg_scores_long <- avg_scores %>%
pivot_longer(cols = 2:3, names_to = \condition\, values_to = \avg_score\)
avg_scores_long$condition <- factor(avg_scores_long$condition,
levels = c(\diff_dTAG_DMSO\, \diff_A485_DMSO\))
# Create the plot
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +
geom_smooth(show.legend = TRUE) + geom_hline(yintercept = 0) + ylim(-0.5, 0.1) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = c(\diff_dTAG_DMSO\ = \pink\, \diff_A485_DMSO\ = \skyblue\)) +
scale_fill_manual(values = c(\diff_dTAG_DMSO\ = \pink\, \diff_A485_DMSO\ = \skyblue\)) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Diff Score\) +
theme(plot.title = element_text(size = 5))
fileName <- paste0(\dist_vs_score_difflinePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4.5, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4.5, height = 3)
print(p4)
dev.off()
}
create_loop_scatterplot <- function(data, figDir, name, Anno2List, diffCutoff){
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% Anno2List)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
### Scatterplot
data$density <- get_density(data$DMSO, data$dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
correlation <- cor(data$DMSO, data$dTAG)
p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +
geom_point(size = 1,
alpha = 1,
stroke = 0) +
scale_color_viridis(option = \D\, guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)) +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, , size = 1,
family = fontType) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, , size = 1,
family = fontType) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, , size = 1,
family = fontType) +
theme_classic() +
#annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\) +
theme(
# legend.position = \none\,
plot.title = element_text(
hjust = 0.5,
size = fontSizeS,
family = fontType
),
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
) +
xlab(\Loop score\nG1.DMSO\) +
ylab(\Loop score\nG1.dTAG\)
num.up <- (summary(data$updown_A485_DMSO))[\UP\]
num.no <- (summary(data$updown_A485_DMSO))[\NO\]
num.down <- (summary(data$updown_A485_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$A485, n = 100)
data <- data %>% dplyr::arrange(density)
correlation <- cor(data$DMSO, data$A485)
p2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +
geom_point(size = 1,
alpha = 1,
stroke = 0) +
scale_color_viridis(option = \D\, guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)) +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 1,
family = fontType) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 1,
family = fontType) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 1,
family = fontType) +
theme_classic() +
# annotate(\text\, x = -0.5, y = 1, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\) +
theme(
# legend.position = \none\,
plot.title = element_text(
hjust = 0.5,
size = fontSizeS,
family = fontType
),
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
) +
xlab(\Loop score\nG1.DMSO\) +
ylab(\Loop score\nG1.A485\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- here(figDir,paste0(\scatterplot_\, name, \_dTAG_vs_DMSO_\, diffCutoff))
svglite(paste0(fileName, \.svg\), width = width, height =height)
print(p1)
dev.off()
png(paste0(fileName, \.png\), width = width, height =height, res = 600, unit = \in\)
print(p1)
dev.off()
fileName <- here(figDir, paste0(\scatterplot_\, name, \_A485_vs_DMSO_\, diffCutoff))
svglite(paste0(fileName, \.svg\), width = width, height =height)
print(p2)
dev.off()
png(paste0(fileName, \.png\), width = width, height =height, res = 600, unit = \in\)
print(p2)
dev.off()
#
#
#
# png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 5*1.5, height = 2.5*1.5)
# print(cowplot::plot_grid(p1, p2, align = \h\))
# dev.off()
#
# svglite(here(figDir, paste0(fileName, \.svg\)),
# width = 5*1.5, height = 2.5*1.5)
# print(cowplot::plot_grid(p1, p2, align = \h\))
# dev.off()
}
make_diff_bedpe <- function(data, name, Anno2List, outDir, diffCutoff){
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% Anno2List)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \UP\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_UP_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \NO\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_NO_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \DOWN\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_DOWN_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \UP\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_UP_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \NO\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_NO_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \DOWN\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_DOWN_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
}
create_dist_vs_avgScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){
avg_scores_long <- data %>%
group_by(distance, Anno2) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) +
geom_smooth(show.legend = TRUE, se = se) +
ylim(0, 0.5) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Score\) +
theme(plot.title = element_text(size = 8))
fileName <- paste0(\dist_vs_score_linePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_dist_vs_avgDiffScore_perTreatment <- function(data, figDir, name, loopList, colorList, se = FALSE){
avg_scores_long <- data %>%
group_by(distance, Anno2) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) +
geom_hline(yintercept = 0) +
geom_smooth(show.legend = TRUE, se = se) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Diff Score\) +
theme(plot.title = element_text(size = 8))
fileName <- paste0(\dist_vs_score_difflinePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_score_barplot_perTreatment <- function(data, figDir, name, loopList, colorList){
data$Anno2 <- factor(data$Anno2, levels = loopList)
p3 <- ggplot(data, aes(x = Anno2, y = score)) +
geom_violin(aes(fill = Anno2), show.legend = FALSE) +
scale_fill_manual(values = colorList) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
ylim(-0.5, 1) +
geom_hline(yintercept = 0, linetype = \dashed\, color = \grey\) +
theme_classic() +
ggtitle(name) +
theme(plot.title = element_text(size = 4))
fileName <- paste0(\score_barPlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 2, height = 4)
print(p3)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 2, height = 4)
print(p3)
}
iterate_loop_functions <- function(data, figDir, groupName, annoList){
# create_loop_dis_vs_score(data, figDir, groupName, annoList)
# create_loop_dis_vs_diffscore(data, figDir, groupName, annoList)
create_loop_scatterplot(data, figDir, groupName, annoList, 0.2)
# create_loop_scatterplot(data, figDir, groupName, annoList, 0.1)
# make_diff_bedpe(data, groupName, annoList, consensusDir, 0.2)
# make_diff_bedpe(data, groupName, annoList, consensusDir, 0.1)
}
```
```
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##### Pure-reg vs Str-reg
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-->\n\n```r\n\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\cl_all\\), unique(data$Anno2))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\cl_structure\\), \n                       c(\\S-S\\, \\S-X\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\s-s\\), \n                       c(\\S-S\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\cl_regulatory\\), \n                       c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\, \\E-E\\, \\E-S\\, \\E-X\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\cl_pe-pe\\), \n                       c(\\P-P\\, \\P-E\\, \\E-E\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\p-pe\\), \n                       c(\\P-P\\, \\P-E\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\p-p\\), \n                       c(\\P-P\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\p-e\\), \n                       c(\\P-E\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\e-e\\), \n                       c(\\E-E\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\x-x\\), \n                       c(\\X-X\\))\n###########\n# Creating differential scatterplot\nView(data)\n\n\n\n#######################\n# Creating figures per each condition\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \\_DMSO\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \\_dTAG\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\n\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \\_A485\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\n\n#######################\n# Creating figures per each condition, differential\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \\_dTAG\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\ncreate_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \\_dTAG_SE\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop, se = TRUE)\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \\_A485\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\n\ncreate_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \\_A485_SE\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop, se = TRUE)\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
iterate_loop_functions(data, figDir, paste0(name, \_\, \cl_all\), unique(data$Anno2))
iterate_loop_functions(data, figDir, paste0(name, \_\, \cl_structure\),
c(\S-S\, \S-X\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \s-s\),
c(\S-S\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \cl_regulatory\),
c(\P-P\, \P-E\, \P-S\, \P-X\, \E-E\, \E-S\, \E-X\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \cl_pe-pe\),
c(\P-P\, \P-E\, \E-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-pe\),
c(\P-P\, \P-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-p\),
c(\P-P\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-e\),
c(\P-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \e-e\),
c(\E-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \x-x\),
c(\X-X\))
###########
# Creating differential scatterplot
View(data)
#######################
# Creating figures per each condition
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_DMSO\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_dTAG\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_A485\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
#######################
# Creating figures per each condition, differential
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG_SE\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop, se = TRUE)
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485_SE\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop, se = TRUE)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\cl_all\\), unique(data$Anno2))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\cl_structure\\), \n                       c(\\S-S\\, \\S-X\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\s-s\\), \n                       c(\\S-S\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\cl_regulatory\\), \n                       c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\, \\E-E\\, \\E-S\\, \\E-X\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\cl_pe-pe\\), \n                       c(\\P-P\\, \\P-E\\, \\E-E\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\p-pe\\), \n                       c(\\P-P\\, \\P-E\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\p-p\\), \n                       c(\\P-P\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\p-e\\), \n                       c(\\P-E\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\e-e\\), \n                       c(\\E-E\\))\niterate_loop_functions(data, figDir, paste0(name, \\_\\, \\x-x\\), \n                       c(\\X-X\\))\n###########\n# Creating differential scatterplot\nView(data)\n\n\n\n#######################\n# Creating figures per each condition\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \\_DMSO\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \\_dTAG\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\n\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \\_A485\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\n\n#######################\n# Creating figures per each condition, differential\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \\_dTAG\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\ncreate_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \\_dTAG_SE\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop, se = TRUE)\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ntemp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\ncreate_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \\_A485\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop)\n\ncreate_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \\_A485_SE\\), c(\\X-X\\,\n                                                                 \\S-X\\, \\S-S\\,\n                                                                 \\E-X\\,\\E-S\\,\\E-E\\,\n                                                                 \\P-E\\,\\P-X\\, \\P-S\\, \\P-P\\), colorListLoop, se = TRUE)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
iterate_loop_functions(data, figDir, paste0(name, \_\, \cl_all\), unique(data$Anno2))
iterate_loop_functions(data, figDir, paste0(name, \_\, \cl_structure\),
c(\S-S\, \S-X\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \s-s\),
c(\S-S\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \cl_regulatory\),
c(\P-P\, \P-E\, \P-S\, \P-X\, \E-E\, \E-S\, \E-X\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \cl_pe-pe\),
c(\P-P\, \P-E\, \E-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-pe\),
c(\P-P\, \P-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-p\),
c(\P-P\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-e\),
c(\P-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \e-e\),
c(\E-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \x-x\),
c(\X-X\))
###########
# Creating differential scatterplot
View(data)
#######################
# Creating figures per each condition
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_DMSO\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_dTAG\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_A485\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
#######################
# Creating figures per each condition, differential
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG_SE\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop, se = TRUE)
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485_SE\), c(\X-X\,
\S-X\, \S-S\,
\E-X\,\E-S\,\E-E\,
\P-E\,\P-X\, \P-S\, \P-P\), colorListLoop, se = TRUE)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.6] Comparing across samples for conesnsus loop
#### Functions
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\_Anno3.tsv\\)))\ndiffCutoff <- 0.2\n\n# Splitting data\ndata.reg <- data %>% dplyr::filter(Anno3 == \\reg\\)\ndata.str <- data %>% dplyr::filter(Anno3 == \\str\\)\n\n# Taking codes from previous function (str)\ndata <- data.str\ndata <- data %>%\n  dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\ndata$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ndata$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n\nnum.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\nnum.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\nnum.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\nnum.all <- num.up + num.no + num.down\nperc.up <- round(num.up / num.all * 100, 2)\nperc.no <- round(num.no / num.all * 100, 2)\nperc.down <- round(num.down / num.all * 100, 2)\n\ndata$density <- get_density(data$DMSO, data$dTAG, n = 100)\ndata <- data %>% dplyr::arrange(density)\np1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))\n\n# Taking codes from previous function (reg)\ndata <- data.reg\ndata <- data %>%\n  dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\ndata$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ndata$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n\nnum.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\nnum.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\nnum.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\nnum.all <- num.up + num.no + num.down\nperc.up <- round(num.up / num.all * 100, 2)\nperc.no <- round(num.no / num.all * 100, 2)\nperc.down <- round(num.down / num.all * 100, 2)\n\ndata$density <- get_density(data$DMSO, data$dTAG, n = 100)\ndata <- data %>% dplyr::arrange(density)\np2 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))  \n  \n\nfileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_pure_str_reg\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 5*1.5, height = 2.5*1.5)\nprint(cowplot::plot_grid(p1, p2, align = \\h\\))\ndev.off()\n\nsvglite(here(figDir, paste0(fileName, \\.svg\\)), \n        width = 5*1.5, height = 2.5*1.5)\nprint(cowplot::plot_grid(p1, p2, align = \\h\\))\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \_Anno3.tsv\)))
diffCutoff <- 0.2
# Splitting data
data.reg <- data %>% dplyr::filter(Anno3 == \reg\)
data.str <- data %>% dplyr::filter(Anno3 == \str\)
# Taking codes from previous function (str)
data <- data.str
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))
# Taking codes from previous function (reg)
data <- data.reg
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
p2 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_pure_str_reg\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\_Anno3.tsv\\)))\ndiffCutoff <- 0.2\n\n# Splitting data\ndata.reg <- data %>% dplyr::filter(Anno3 == \\reg\\)\ndata.str <- data %>% dplyr::filter(Anno3 == \\str\\)\n\n# Taking codes from previous function (str)\ndata <- data.str\ndata <- data %>%\n  dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\ndata$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ndata$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n\nnum.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\nnum.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\nnum.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\nnum.all <- num.up + num.no + num.down\nperc.up <- round(num.up / num.all * 100, 2)\nperc.no <- round(num.no / num.all * 100, 2)\nperc.down <- round(num.down / num.all * 100, 2)\n\ndata$density <- get_density(data$DMSO, data$dTAG, n = 100)\ndata <- data %>% dplyr::arrange(density)\np1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))\n\n# Taking codes from previous function (reg)\ndata <- data.reg\ndata <- data %>%\n  dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\ndata$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ndata$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n\nnum.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\nnum.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\nnum.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\nnum.all <- num.up + num.no + num.down\nperc.up <- round(num.up / num.all * 100, 2)\nperc.no <- round(num.no / num.all * 100, 2)\nperc.down <- round(num.down / num.all * 100, 2)\n\ndata$density <- get_density(data$DMSO, data$dTAG, n = 100)\ndata <- data %>% dplyr::arrange(density)\np2 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))  \n  \n\nfileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_pure_str_reg\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 5*1.5, height = 2.5*1.5)\nprint(cowplot::plot_grid(p1, p2, align = \\h\\))\ndev.off()\n\nsvglite(here(figDir, paste0(fileName, \\.svg\\)), \n        width = 5*1.5, height = 2.5*1.5)\nprint(cowplot::plot_grid(p1, p2, align = \\h\\))\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubmFtZSA8LSBcXGNocm9tb19jb25zX2Fubm9IaWVyYXJjaHlcXFxuZGF0YSA8LSBmcmVhZChoZXJlKGNvbnNlbnN1c0RpciwgcGFzdGUwKG5hbWUsIFxcX0Fubm8zLnRzdlxcKSkpXG5kaWZmQ3V0b2ZmIDwtIDAuMlxuXG4jIFNwbGl0dGluZyBkYXRhXG5kYXRhLnJlZyA8LSBkYXRhICU+JSBkcGx5cjo6ZmlsdGVyKEFubm8zID09IFxccmVnXFwpXG5kYXRhLnN0ciA8LSBkYXRhICU+JSBkcGx5cjo6ZmlsdGVyKEFubm8zID09IFxcc3RyXFwpXG5cbiMgVGFraW5nIGNvZGVzIGZyb20gcHJldmlvdXMgZnVuY3Rpb24gKHN0cilcbmRhdGEgPC0gZGF0YS5zdHJcbmRhdGEgPC0gZGF0YSAlPiVcbiAgZHBseXI6Om11dGF0ZSh1cGRvd25fZFRBR19ETVNPID0gaWZlbHNlKGRpZmZfZFRBR19ETVNPID4gZGlmZkN1dG9mZiwgXFxVUFxcLCBcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGlmZWxzZShkaWZmX2RUQUdfRE1TTyA+IC1kaWZmQ3V0b2ZmLCBcXE5PXFwsIFxcRE9XTlxcKSksXG4gICAgICAgICAgICAgICAgdXBkb3duX0E0ODVfRE1TTyA9IGlmZWxzZShkaWZmX0E0ODVfRE1TTyA+IGRpZmZDdXRvZmYsIFxcVVBcXCwgXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBpZmVsc2UoZGlmZl9BNDg1X0RNU08gPiAtZGlmZkN1dG9mZiwgXFxOT1xcLCBcXERPV05cXCkpKVxuZGF0YSR1cGRvd25fZFRBR19ETVNPIDwtIGZhY3RvcihkYXRhJHVwZG93bl9kVEFHX0RNU08sIGxldmVscyA9IGMoXFxVUFxcLCBcXE5PXFwsIFxcRE9XTlxcKSlcbmRhdGEkdXBkb3duX0E0ODVfRE1TTyA8LSBmYWN0b3IoZGF0YSR1cGRvd25fQTQ4NV9ETVNPLCBsZXZlbHMgPSBjKFxcVVBcXCwgXFxOT1xcLCBcXERPV05cXCkpXG5cbm51bS51cCA8LSAoc3VtbWFyeShkYXRhJHVwZG93bl9kVEFHX0RNU08pKVtcXFVQXFxdXG5udW0ubm8gPC0gKHN1bW1hcnkoZGF0YSR1cGRvd25fZFRBR19ETVNPKSlbXFxOT1xcXVxubnVtLmRvd24gPC0gKHN1bW1hcnkoZGF0YSR1cGRvd25fZFRBR19ETVNPKSlbXFxET1dOXFxdXG5udW0uYWxsIDwtIG51bS51cCArIG51bS5ubyArIG51bS5kb3duXG5wZXJjLnVwIDwtIHJvdW5kKG51bS51cCAvIG51bS5hbGwgKiAxMDAsIDIpXG5wZXJjLm5vIDwtIHJvdW5kKG51bS5ubyAvIG51bS5hbGwgKiAxMDAsIDIpXG5wZXJjLmRvd24gPC0gcm91bmQobnVtLmRvd24gLyBudW0uYWxsICogMTAwLCAyKVxuXG5kYXRhJGRlbnNpdHkgPC0gZ2V0X2RlbnNpdHkoZGF0YSRETVNPLCBkYXRhJGRUQUcsIG4gPSAxMDApXG5kYXRhIDwtIGRhdGEgJT4lIGRwbHlyOjphcnJhbmdlKGRlbnNpdHkpXG5wMSA8LSBnZ3Bsb3QoZGF0YSwgYWVzKHggPSBETVNPLCB5ID0gZFRBRywgY29sb3IgPSBkZW5zaXR5KSkgK1xuICBnZW9tX3BvaW50KHNob3cubGVnZW5kID0gRkFMU0UpICsgXG4gIHNjYWxlX2NvbG9yX3ZpcmlkaXMoKSArXG4gIHhsaW0oLTAuNSwgMSkgKyB5bGltKC0wLjUsIDEpICsgY29vcmRfZml4ZWQoKSArXG4gIGdlb21fYWJsaW5lKHNsb3BlID0gMSwgaW50ZXJjZXB0ID0gMCwgY29sID0gXFxncmV5NTBcXCwgbGluZXR5cGUgPSBcXGRhc2hlZFxcKSArXG4gIGdlb21fYWJsaW5lKHNsb3BlID0gMSwgaW50ZXJjZXB0ID0gLWRpZmZDdXRvZmYsIGNvbCA9IFxcZ3JleVxcLCBsaW5ldHlwZSA9IFxcZG90dGVkXFwpICtcbiAgZ2VvbV9hYmxpbmUoc2xvcGUgPSAxLCBpbnRlcmNlcHQgPSBkaWZmQ3V0b2ZmLCBjb2wgPSBcXGdyZXlcXCwgbGluZXR5cGUgPSBcXGRvdHRlZFxcKSArXG4gIGdlb21faGxpbmUoeWludGVyY2VwdCA9IDAsIGFscGhhID0gMC41LCBjb2xvciA9IFxcZ3JleVxcKSArXG4gIGdlb21fdmxpbmUoeGludGVyY2VwdCA9IDAsIGFscGhhID0gMC41LCBjb2xvciA9IFxcZ3JleVxcKSArXG4gIGFubm90YXRlKFxcdGV4dFxcLCB4ID0gLTAuNSwgeSA9IDEsIGxhYmVsID0gcGFzdGUwKFxcVVA6IFxcLCBudW0udXAsIFxcIChcXCwgcGVyYy51cCwgXFwlKVxcKSwgXG4gICAgICAgICAgIGNvbG9yID0gXFxibGFja1xcLCBoanVzdCA9IDAsIHNpemUgPSAzKSArXG4gIGFubm90YXRlKFxcdGV4dFxcLCB4ID0gLTAuNSwgeSA9IDEtMC4xLCBsYWJlbCA9IHBhc3RlMChcXE5POiBcXCwgbnVtLm5vLCBcXCAoXFwsIHBlcmMubm8sIFxcJSlcXCksIFxuICAgICAgICAgICBjb2xvciA9IFxcYmxhY2tcXCwgaGp1c3QgPSAwLCBzaXplID0gMykgK1xuICBhbm5vdGF0ZShcXHRleHRcXCwgeCA9IC0wLjUsIHkgPSAxLTAuMiwgbGFiZWwgPSBwYXN0ZTAoXFxET1dOOiBcXCwgbnVtLmRvd24sIFxcIChcXCwgcGVyYy5kb3duLCBcXCUpXFwpLCBcbiAgICAgICAgICAgY29sb3IgPSBcXGJsYWNrXFwsIGhqdXN0ID0gMCwgc2l6ZSA9IDMpICtcbiAgdGhlbWVfY2xhc3NpYygpICsgZ2d0aXRsZShuYW1lKSArIHRoZW1lKHBsb3QudGl0bGUgPSBlbGVtZW50X3RleHQoc2l6ZSA9IDUpKVxuXG4jIFRha2luZyBjb2RlcyBmcm9tIHByZXZpb3VzIGZ1bmN0aW9uIChyZWcpXG5kYXRhIDwtIGRhdGEucmVnXG5kYXRhIDwtIGRhdGEgJT4lXG4gIGRwbHlyOjptdXRhdGUodXBkb3duX2RUQUdfRE1TTyA9IGlmZWxzZShkaWZmX2RUQUdfRE1TTyA+IGRpZmZDdXRvZmYsIFxcVVBcXCwgXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBpZmVsc2UoZGlmZl9kVEFHX0RNU08gPiAtZGlmZkN1dG9mZiwgXFxOT1xcLCBcXERPV05cXCkpLFxuICAgICAgICAgICAgICAgIHVwZG93bl9BNDg1X0RNU08gPSBpZmVsc2UoZGlmZl9BNDg1X0RNU08gPiBkaWZmQ3V0b2ZmLCBcXFVQXFwsIFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgaWZlbHNlKGRpZmZfQTQ4NV9ETVNPID4gLWRpZmZDdXRvZmYsIFxcTk9cXCwgXFxET1dOXFwpKSlcbmRhdGEkdXBkb3duX2RUQUdfRE1TTyA8LSBmYWN0b3IoZGF0YSR1cGRvd25fZFRBR19ETVNPLCBsZXZlbHMgPSBjKFxcVVBcXCwgXFxOT1xcLCBcXERPV05cXCkpXG5kYXRhJHVwZG93bl9BNDg1X0RNU08gPC0gZmFjdG9yKGRhdGEkdXBkb3duX0E0ODVfRE1TTywgbGV2ZWxzID0gYyhcXFVQXFwsIFxcTk9cXCwgXFxET1dOXFwpKVxuXG5udW0udXAgPC0gKHN1bW1hcnkoZGF0YSR1cGRvd25fZFRBR19ETVNPKSlbXFxVUFxcXVxubnVtLm5vIDwtIChzdW1tYXJ5KGRhdGEkdXBkb3duX2RUQUdfRE1TTykpW1xcTk9cXF1cbm51bS5kb3duIDwtIChzdW1tYXJ5KGRhdGEkdXBkb3duX2RUQUdfRE1TTykpW1xcRE9XTlxcXVxubnVtLmFsbCA8LSBudW0udXAgKyBudW0ubm8gKyBudW0uZG93blxucGVyYy51cCA8LSByb3VuZChudW0udXAgLyBudW0uYWxsICogMTAwLCAyKVxucGVyYy5ubyA8LSByb3VuZChudW0ubm8gLyBudW0uYWxsICogMTAwLCAyKVxucGVyYy5kb3duIDwtIHJvdW5kKG51bS5kb3duIC8gbnVtLmFsbCAqIDEwMCwgMilcblxuZGF0YSRkZW5zaXR5IDwtIGdldF9kZW5zaXR5KGRhdGEkRE1TTywgZGF0YSRkVEFHLCBuID0gMTAwKVxuZGF0YSA8LSBkYXRhICU+JSBkcGx5cjo6YXJyYW5nZShkZW5zaXR5KVxucDIgPC0gZ2dwbG90KGRhdGEsIGFlcyh4ID0gRE1TTywgeSA9IGRUQUcsIGNvbG9yID0gZGVuc2l0eSkpICtcbiAgZ2VvbV9wb2ludChzaG93LmxlZ2VuZCA9IEZBTFNFKSArIFxuICBzY2FsZV9jb2xvcl92aXJpZGlzKCkgK1xuICB4bGltKC0wLjUsIDEpICsgeWxpbSgtMC41LCAxKSArIGNvb3JkX2ZpeGVkKCkgK1xuICBnZW9tX2FibGluZShzbG9w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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \_Anno3.tsv\)))
diffCutoff <- 0.2
# Splitting data
data.reg <- data %>% dplyr::filter(Anno3 == \reg\)
data.str <- data %>% dplyr::filter(Anno3 == \str\)
# Taking codes from previous function (str)
data <- data.str
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))
# Taking codes from previous function (reg)
data <- data.reg
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
p2 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_pure_str_reg\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Relaxed annotation
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\_Anno3.tsv\\)))\n\ndata <- data %>% dplyr::mutate(size = start2 - start1)\n\ndata_under50kb <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\), size < 50*1000, Anno3 == \\reg\\)\ndata_under200kb <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\), size >= 50*1000, size < 200*1000, Anno3 == \\reg\\)\ndata_over200kb <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\), size >= 200*1000, Anno3 == \\reg\\)\n\n########\n\ndata <- data_under50kb\n\ndiffCutoff <- 0.2\ndata <- data %>%\n  dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\ndata$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ndata$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n\n####\nnum.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\nnum.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\nnum.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\nnum.all <- num.up + num.no + num.down\nperc.up <- round(num.up / num.all * 100, 2)\nperc.no <- round(num.no / num.all * 100, 2)\nperc.down <- round(num.down / num.all * 100, 2)\n\ndata$density <- get_density(data$DMSO, data$dTAG, n = 100)\ndata <- data %>% dplyr::arrange(density)\np1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))  \n  \n\n####\nnum.up <- (summary(data$updown_A485_DMSO))[\\UP\\]\nnum.no <- (summary(data$updown_A485_DMSO))[\\NO\\]\nnum.down <- (summary(data$updown_A485_DMSO))[\\DOWN\\]\nnum.all <- num.up + num.no + num.down\nperc.up <- round(num.up / num.all * 100, 2)\nperc.no <- round(num.no / num.all * 100, 2)\nperc.down <- round(num.down / num.all * 100, 2)\n\ndata$density <- get_density(data$DMSO, data$A485, n = 100)\ndata <- data %>% dplyr::arrange(density)\np2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +\n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))  \n  \n\nfileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_regunder50kb_pure\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 5*1.5, height = 2.5*1.5)\nprint(cowplot::plot_grid(p1, p2, align = \\h\\))\ndev.off()\n\nsvglite(here(figDir, paste0(fileName, \\.svg\\)), \n        width = 5*1.5, height = 2.5*1.5)\nprint(cowplot::plot_grid(p1, p2, align = \\h\\))\ndev.off()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \_Anno3.tsv\)))
data <- data %>% dplyr::mutate(size = start2 - start1)
data_under50kb <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\), size < 50*1000, Anno3 == \reg\)
data_under200kb <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\), size >= 50*1000, size < 200*1000, Anno3 == \reg\)
data_over200kb <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\), size >= 200*1000, Anno3 == \reg\)
########
data <- data_under50kb
diffCutoff <- 0.2
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
####
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))
####
num.up <- (summary(data$updown_A485_DMSO))[\UP\]
num.no <- (summary(data$updown_A485_DMSO))[\NO\]
num.down <- (summary(data$updown_A485_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$A485, n = 100)
data <- data %>% dplyr::arrange(density)
p2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_regunder50kb_pure\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\_Anno3.tsv\\)))\n\ndata <- data %>% dplyr::mutate(size = start2 - start1)\n\ndata_under50kb <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\), size < 50*1000, Anno3 == \\reg\\)\ndata_under200kb <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\), size >= 50*1000, size < 200*1000, Anno3 == \\reg\\)\ndata_over200kb <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\), size >= 200*1000, Anno3 == \\reg\\)\n\n########\n\ndata <- data_under50kb\n\ndiffCutoff <- 0.2\ndata <- data %>%\n  dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\ndata$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ndata$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\n\n####\nnum.up <- (summary(data$updown_dTAG_DMSO))[\\UP\\]\nnum.no <- (summary(data$updown_dTAG_DMSO))[\\NO\\]\nnum.down <- (summary(data$updown_dTAG_DMSO))[\\DOWN\\]\nnum.all <- num.up + num.no + num.down\nperc.up <- round(num.up / num.all * 100, 2)\nperc.no <- round(num.no / num.all * 100, 2)\nperc.down <- round(num.down / num.all * 100, 2)\n\ndata$density <- get_density(data$DMSO, data$dTAG, n = 100)\ndata <- data %>% dplyr::arrange(density)\np1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +\n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))  \n  \n\n####\nnum.up <- (summary(data$updown_A485_DMSO))[\\UP\\]\nnum.no <- (summary(data$updown_A485_DMSO))[\\NO\\]\nnum.down <- (summary(data$updown_A485_DMSO))[\\DOWN\\]\nnum.all <- num.up + num.no + num.down\nperc.up <- round(num.up / num.all * 100, 2)\nperc.no <- round(num.no / num.all * 100, 2)\nperc.down <- round(num.down / num.all * 100, 2)\n\ndata$density <- get_density(data$DMSO, data$A485, n = 100)\ndata <- data %>% dplyr::arrange(density)\np2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +\n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() +\n  xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  annotate(\\text\\, x = -0.5, y = 1, label = paste0(\\UP: \\, num.up, \\ (\\, perc.up, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.1, label = paste0(\\NO: \\, num.no, \\ (\\, perc.no, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  annotate(\\text\\, x = -0.5, y = 1-0.2, label = paste0(\\DOWN: \\, num.down, \\ (\\, perc.down, \\%)\\), \n           color = \\black\\, hjust = 0, size = 3) +\n  theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))  \n  \n\nfileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_regunder50kb_pure\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 5*1.5, height = 2.5*1.5)\nprint(cowplot::plot_grid(p1, p2, align = \\h\\))\ndev.off()\n\nsvglite(here(figDir, paste0(fileName, \\.svg\\)), \n        width = 5*1.5, height = 2.5*1.5)\nprint(cowplot::plot_grid(p1, p2, align = \\h\\))\ndev.off()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \_Anno3.tsv\)))
data <- data %>% dplyr::mutate(size = start2 - start1)
data_under50kb <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\), size < 50*1000, Anno3 == \reg\)
data_under200kb <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\), size >= 50*1000, size < 200*1000, Anno3 == \reg\)
data_over200kb <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\), size >= 200*1000, Anno3 == \reg\)
########
data <- data_under50kb
diffCutoff <- 0.2
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
####
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
p1 <- ggplot(data, aes(x = DMSO, y = dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))
####
num.up <- (summary(data$updown_A485_DMSO))[\UP\]
num.no <- (summary(data$updown_A485_DMSO))[\NO\]
num.down <- (summary(data$updown_A485_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$DMSO, data$A485, n = 100)
data <- data %>% dplyr::arrange(density)
p2 <- ggplot(data, aes(x = DMSO, y = A485, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
xlim(-0.5, 1) + ylim(-0.5, 1) + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = -0.5, y = 1, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = -0.5, y = 1-0.2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5))
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_regunder50kb_pure\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### Pure-reg vs str-reg
<!-- rnb-text-end -->
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name <- \chromo_cons_annoPromoter\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
iterate_loop_functions(data, figDir, paste0(name, \_\, \all\), unique(data$Anno2))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-p\), c(\P-P\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-n\), c(\P-N\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \n-n\), c(\N-N\))
colorList <- colorListPromoter
loopList <- c(\N-N\, \P-N\, \P-P\)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
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<!-- rnb-output-begin 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-->
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```r
```r
name <- \chromo_cons_annoPromoter\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
iterate_loop_functions(data, figDir, paste0(name, \_\, \all\), unique(data$Anno2))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-p\), c(\P-P\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \p-n\), c(\P-N\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \n-n\), c(\N-N\))
colorList <- colorListPromoter
loopList <- c(\N-N\, \P-N\, \P-P\)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
```
```
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<!-- rnb-output-end -->
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##### 50kb vs 200 kb
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<!-- rnb-output-begin 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 -->
name <- \chromo_cons_annoEnhancer\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
iterate_loop_functions(data, figDir, paste0(name, \_\, \all\), unique(data$Anno2))
iterate_loop_functions(data, figDir, paste0(name, \_\, \e-e\), c(\E-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \e-n\), c(\E-N\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \n-n\), c(\N-N\))
colorList <- colorListEnhancer
loopList <- c(\N-N\, \E-N\, \E-E\)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
name <- \chromo_cons_annoEnhancer\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
iterate_loop_functions(data, figDir, paste0(name, \_\, \all\), unique(data$Anno2))
iterate_loop_functions(data, figDir, paste0(name, \_\, \e-e\), c(\E-E\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \e-n\), c(\E-N\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \n-n\), c(\N-N\))
colorList <- colorListEnhancer
loopList <- c(\N-N\, \E-N\, \E-E\)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### One feature annotation - promoter
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
name <- \chromo_cons_annoStructure\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
iterate_loop_functions(data, figDir, paste0(name, \_\, \all\), unique(data$Anno2))
iterate_loop_functions(data, figDir, paste0(name, \_\, \s-s\), c(\S-S\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \s-n\), c(\S-N\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \n-n\), c(\N-N\))
colorList <- colorListStructure
loopList <- c(\N-N\, \S-N\, \S-S\)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
name <- \chromo_cons_annoStructure\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
iterate_loop_functions(data, figDir, paste0(name, \_\, \all\), unique(data$Anno2))
iterate_loop_functions(data, figDir, paste0(name, \_\, \s-s\), c(\S-S\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \s-n\), c(\S-N\))
iterate_loop_functions(data, figDir, paste0(name, \_\, \n-n\), c(\N-N\))
colorList <- colorListStructure
loopList <- c(\N-N\, \S-N\, \S-S\)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_DMSO\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
create_score_barplot_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_dTAG\),
loopList, colorList, se = TRUE)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment(temp, figDir, paste0(name, \_A485\),
loopList, colorList, se = TRUE)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### One feature annotation - enhancer
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
data <- fread(here(consensusDir, \chromo_cons_score.tsv\)) %>%
dplyr::mutate(distance = start2 - start1,
res = end1 - start1)
p1 <- ggplot(data, aes(x = distance, fill = factor(res))) +
geom_histogram(binwidth = 50000, alpha = 1) +
labs(title = \Distribution of Distance by Resolution\,
x = \Distance\,
y = \Counts\) +
facet_wrap(~ res, ncol = 1, scales = \free\) +
scale_x_continuous(labels = label_kb_mb, limits = c(0, 5e6)) +
theme_classic() +
theme(legend.position = \none\,
plot.title = element_text(size = 8)) # Removes the legend
png(here(figDir, paste0(\consensus_dist_per_res.png\)),
res = 600, units = \in\, width = 3, height = 6)
print(p1)
dev.off()
p2 <- ggplot(data, aes(x = distance, fill = factor(res))) +
geom_histogram(binwidth = 50000, alpha = 1) +
labs(title = \Distribution of Distance by Resolution\,
x = \Distance\,
y = \Counts\) +
facet_wrap(~ res, ncol = 1, scales = \free\) +
scale_x_continuous(labels = label_kb_mb, limits = c(0, 2e6)) +
theme_classic() +
theme(legend.position = \none\,
plot.title = element_text(size = 8)) # Removes the legend
png(here(figDir, paste0(\consensus_dist_per_res_2mb.png\)),
res = 600, units = \in\, width = 3, height = 6)
print(p2)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
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```r
```r
data <- fread(here(consensusDir, \chromo_cons_score.tsv\)) %>%
dplyr::mutate(distance = start2 - start1,
res = end1 - start1)
p1 <- ggplot(data, aes(x = distance, fill = factor(res))) +
geom_histogram(binwidth = 50000, alpha = 1) +
labs(title = \Distribution of Distance by Resolution\,
x = \Distance\,
y = \Counts\) +
facet_wrap(~ res, ncol = 1, scales = \free\) +
scale_x_continuous(labels = label_kb_mb, limits = c(0, 5e6)) +
theme_classic() +
theme(legend.position = \none\,
plot.title = element_text(size = 8)) # Removes the legend
png(here(figDir, paste0(\consensus_dist_per_res.png\)),
res = 600, units = \in\, width = 3, height = 6)
print(p1)
dev.off()
p2 <- ggplot(data, aes(x = distance, fill = factor(res))) +
geom_histogram(binwidth = 50000, alpha = 1) +
labs(title = \Distribution of Distance by Resolution\,
x = \Distance\,
y = \Counts\) +
facet_wrap(~ res, ncol = 1, scales = \free\) +
scale_x_continuous(labels = label_kb_mb, limits = c(0, 2e6)) +
theme_classic() +
theme(legend.position = \none\,
plot.title = element_text(size = 8)) # Removes the legend
png(here(figDir, paste0(\consensus_dist_per_res_2mb.png\)),
res = 600, units = \in\, width = 3, height = 6)
print(p2)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### One feature annotation - structure
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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### Distance vs score
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, dTAG, A485)
avg_scores <- temp %>%
group_by(distance) %>%
summarise(across(starts_with(\DMSO\):starts_with(\A485\), mean, na.rm = TRUE))
avg_scores_long <- avg_scores %>%
pivot_longer(cols = DMSO:A485, names_to = \condition\, values_to = \avg_score\)
avg_scores_long$condition <- factor(avg_scores_long$condition, levels = c(\DMSO\, \dTAG\, \A485\))
# Create the plot
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +
geom_smooth(show.legend = TRUE) + ylim(0, 0.5) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = c(\DMSO\ = \grey\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
scale_fill_manual(values = c(\DMSO\ = \grey80\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
labs(title = paste0(\Distance vs. Average Score```r
### Distance vs score
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, DMSO, dTAG, A485)
avg_scores <- temp %>%
group_by(distance) %>%
summarise(across(starts_with(\DMSO\):starts_with(\A485\), mean, na.rm = TRUE))
avg_scores_long <- avg_scores %>%
pivot_longer(cols = DMSO:A485, names_to = \condition\, values_to = \avg_score\)
avg_scores_long$condition <- factor(avg_scores_long$condition, levels = c(\DMSO\, \dTAG\, \A485\))
# Create the plot
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = condition, fill = condition)) +
geom_smooth(show.legend = TRUE) + ylim(0, 0.5) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = c(\DMSO\ = \grey\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
scale_fill_manual(values = c(\DMSO\ = \grey80\, \dTAG\ = \pink\, \A485\ = \skyblue\)) +
labs(title = paste0(\Distance vs. Average Score
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBBbm5vdGF0aW5nIGdlbmVzIGJhc2VkIG9uIHByb21vdGVyIGFuY2hvclxubmFtZSA8LSBcImNocm9tb19jb25zX2Fubm9IaWVyYXJjaHlcIlxuZGF0YSA8LSBmcmVhZChoZXJlKGNvbnNlbnN1c0RpciwgcGFzdGUwKG5hbWUsIFwiLnRzdlwiKSkpXG5gYGAifQ== -->
```r
# Annotating genes based on promoter anchor
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
```
<!-- rnb-source-end -->```r
# Annotating genes based on promoter anchor
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nflankSize <- 2500\ngene.TSS.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)\ncolnames(gene.TSS.tb) <- c(\\chr\\, \\start\\, \\end\\, \\strand\\, \\gene\\, \\ensembl\\)\n\nfwrite(gene.TSS.tb, here(refDir, \\mm10_GRCm38.p6_TSS2.5kb.bed\\), sep = \\\\t\\, col.names = FALSE, row.names = FALSE)\n\nfindOverlapGene <- function(gene.TSS.tb, chrom1, start1, end1){\n  temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%\n    dplyr::filter((start <= end1) & (end >= start1))\n  return(temp$gene)\n}\nfindOverlapEnsembl <- function(gene.TSS.tb, chrom1, start1, end1){\n  temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%\n    dplyr::filter((start <= end1) & (end >= start1))\n  return(temp$ensembl)\n}\n\ntemp <- data %>% rowwise() %>%\n  dplyr::mutate(A1_gene = ifelse(A1 == \\P\\,\n                                 list(findOverlapGene(gene.TSS.tb, chrom1, start1, end1)),\n                                 NA),\n                A2_gene = ifelse(A2 == \\P\\,\n                                 list(findOverlapGene(gene.TSS.tb, chrom2, start2, end2)),\n                                 NA),\n                gene = list(unique(c(A1_gene, A2_gene))))\n\ntemp_p_n <- temp %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% dplyr::select(-c(\\A1_gene\\, \\A2_gene\\))\nfwrite(temp_p_n, here(consensusDir, paste0(name, \\_p-n_geneList.tsv\\)), sep = \\\\t\\)\n\ntemp_p_pe <- temp %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\)) %>% dplyr::select(-c(\\A1_gene\\, \\A2_gene\\))\nfwrite(temp_p_pe, here(consensusDir, paste0(name, \\_p-pe_geneList.tsv\\)), sep = \\\\t\\)\n\ntemp <- data %>% rowwise() %>%\n  dplyr::mutate(A1_gene = ifelse(A1 == \\P\\,\n                                 list(findOverlapEnsembl(gene.TSS.tb, chrom1, start1, end1)),\n                                 NA),\n                A2_gene = ifelse(A2 == \\P\\,\n                                 list(findOverlapEnsembl(gene.TSS.tb, chrom2, start2, end2)),\n                                 NA),\n                gene = list(unique(c(A1_gene, A2_gene))))\n\ntemp_p_n <- temp %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% dplyr::select(-c(\\A1_gene\\, \\A2_gene\\))\nfwrite(temp_p_n, here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)), sep = \\\\t\\)\n\ntemp_p_pe <- temp %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\)) %>% dplyr::select(-c(\\A1_gene\\, \\A2_gene\\))\nfwrite(temp_p_pe, here(consensusDir, paste0(name, \\_p-pe_ensemblList.tsv\\)), sep = \\\\t\\)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
flankSize <- 2500
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)
colnames(gene.TSS.tb) <- c(\chr\, \start\, \end\, \strand\, \gene\, \ensembl\)
fwrite(gene.TSS.tb, here(refDir, \mm10_GRCm38.p6_TSS2.5kb.bed\), sep = \\t\, col.names = FALSE, row.names = FALSE)
findOverlapGene <- function(gene.TSS.tb, chrom1, start1, end1){
temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%
dplyr::filter((start <= end1) & (end >= start1))
return(temp$gene)
}
findOverlapEnsembl <- function(gene.TSS.tb, chrom1, start1, end1){
temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%
dplyr::filter((start <= end1) & (end >= start1))
return(temp$ensembl)
}
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1 == \P\,
list(findOverlapGene(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2 == \P\,
list(findOverlapGene(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \P-S\, \P-X\)) %>% dplyr::select(-c(\A1_gene\, \A2_gene\))
fwrite(temp_p_n, here(consensusDir, paste0(name, \_p-n_geneList.tsv\)), sep = \\t\)
temp_p_pe <- temp %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\)) %>% dplyr::select(-c(\A1_gene\, \A2_gene\))
fwrite(temp_p_pe, here(consensusDir, paste0(name, \_p-pe_geneList.tsv\)), sep = \\t\)
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1 == \P\,
list(findOverlapEnsembl(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2 == \P\,
list(findOverlapEnsembl(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \P-S\, \P-X\)) %>% dplyr::select(-c(\A1_gene\, \A2_gene\))
fwrite(temp_p_n, here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)), sep = \\t\)
temp_p_pe <- temp %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\)) %>% dplyr::select(-c(\A1_gene\, \A2_gene\))
fwrite(temp_p_pe, here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)), sep = \\t\)
<!-- rnb-output-end -->
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flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)\ncolnames(gene.TSS.tb) <- c(\\chr\\, \\start\\, \\end\\, \\strand\\, \\gene\\, \\ensembl\\)\n\nfwrite(gene.TSS.tb, here(refDir, \\mm10_GRCm38.p6_TSS2.5kb.bed\\), sep = \\\\t\\, col.names = FALSE, row.names = FALSE)\n\nfindOverlapGene <- function(gene.TSS.tb, chrom1, start1, end1){\n  temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%\n    dplyr::filter((start <= end1) & (end >= start1))\n  return(temp$gene)\n}\nfindOverlapEnsembl <- function(gene.TSS.tb, chrom1, start1, end1){\n  temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%\n    dplyr::filter((start <= end1) & (end >= start1))\n  return(temp$ensembl)\n}\n\ntemp <- data %>% rowwise() %>%\n  dplyr::mutate(A1_gene = ifelse(A1 == \\P\\,\n                                 list(findOverlapGene(gene.TSS.tb, chrom1, start1, end1)),\n                                 NA),\n                A2_gene = ifelse(A2 == \\P\\,\n                                 list(findOverlapGene(gene.TSS.tb, chrom2, start2, end2)),\n                                 NA),\n                gene = list(unique(c(A1_gene, A2_gene))))\n\ntemp_p_n <- temp %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% dplyr::select(-c(\\A1_gene\\, \\A2_gene\\))\nfwrite(temp_p_n, here(consensusDir, paste0(name, \\_p-n_geneList.tsv\\)), sep = \\\\t\\)\n\ntemp_p_pe <- temp %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\)) %>% dplyr::select(-c(\\A1_gene\\, \\A2_gene\\))\nfwrite(temp_p_pe, here(consensusDir, paste0(name, \\_p-pe_geneList.tsv\\)), sep = \\\\t\\)\n\ntemp <- data %>% rowwise() %>%\n  dplyr::mutate(A1_gene = ifelse(A1 == \\P\\,\n                                 list(findOverlapEnsembl(gene.TSS.tb, chrom1, start1, end1)),\n                                 NA),\n                A2_gene = ifelse(A2 == \\P\\,\n                                 list(findOverlapEnsembl(gene.TSS.tb, chrom2, start2, end2)),\n                                 NA),\n                gene = list(unique(c(A1_gene, A2_gene))))\n\ntemp_p_n <- temp %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% dplyr::select(-c(\\A1_gene\\, \\A2_gene\\))\nfwrite(temp_p_n, here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)), sep = \\\\t\\)\n\ntemp_p_pe <- temp %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\)) %>% dplyr::select(-c(\\A1_gene\\, \\A2_gene\\))\nfwrite(temp_p_pe, here(consensusDir, paste0(name, \\_p-pe_ensemblList.tsv\\)), sep = \\\\t\\)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
flankSize <- 2500
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)
colnames(gene.TSS.tb) <- c(\chr\, \start\, \end\, \strand\, \gene\, \ensembl\)
fwrite(gene.TSS.tb, here(refDir, \mm10_GRCm38.p6_TSS2.5kb.bed\), sep = \\t\, col.names = FALSE, row.names = FALSE)
findOverlapGene <- function(gene.TSS.tb, chrom1, start1, end1){
temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%
dplyr::filter((start <= end1) & (end >= start1))
return(temp$gene)
}
findOverlapEnsembl <- function(gene.TSS.tb, chrom1, start1, end1){
temp <- gene.TSS.tb %>% dplyr::filter(chr == chrom1) %>%
dplyr::filter((start <= end1) & (end >= start1))
return(temp$ensembl)
}
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1 == \P\,
list(findOverlapGene(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2 == \P\,
list(findOverlapGene(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \P-S\, \P-X\)) %>% dplyr::select(-c(\A1_gene\, \A2_gene\))
fwrite(temp_p_n, here(consensusDir, paste0(name, \_p-n_geneList.tsv\)), sep = \\t\)
temp_p_pe <- temp %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\)) %>% dplyr::select(-c(\A1_gene\, \A2_gene\))
fwrite(temp_p_pe, here(consensusDir, paste0(name, \_p-pe_geneList.tsv\)), sep = \\t\)
temp <- data %>% rowwise() %>%
dplyr::mutate(A1_gene = ifelse(A1 == \P\,
list(findOverlapEnsembl(gene.TSS.tb, chrom1, start1, end1)),
NA),
A2_gene = ifelse(A2 == \P\,
list(findOverlapEnsembl(gene.TSS.tb, chrom2, start2, end2)),
NA),
gene = list(unique(c(A1_gene, A2_gene))))
temp_p_n <- temp %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \P-S\, \P-X\)) %>% dplyr::select(-c(\A1_gene\, \A2_gene\))
fwrite(temp_p_n, here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)), sep = \\t\)
temp_p_pe <- temp %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\)) %>% dplyr::select(-c(\A1_gene\, \A2_gene\))
fwrite(temp_p_pe, here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)), sep = \\t\)
```
```
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<!-- rnb-text-begin -->
### [2.8] Annotating genes to P-P and P-E loops
#### Link gene to promoter anchor
Here, anchor was linked to a gene based on whether the anchor overlaps with TSS +- 2.5kb region
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-->\n\n```r\ncreate_dist_barplot <- function(data, figDir, name, note, loopList, diffCutoff){\n  temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%\n    dplyr::filter(Anno2 %in% loopList,\n                  updown_dTAG_DMSO %in% c(\\UP\\, \\NO\\, \\DOWN\\))\n  p <- ggplot(temp, aes(x = updown_dTAG_DMSO, y = distance)) +\n    geom_violin(aes(fill = updown_dTAG_DMSO)) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = updown_dTAG_DMSO), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + ggtitle(note) +\n    scale_y_continuous(labels = label_kb_mb)\n  fileName <- paste0(\\size_barplot_\\, name, \\_dTAG_vs_DMSO_\\, note, \\_\\, diffCutoff)\n  height <- 3\n  width <- 4\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n  \n  \n  temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%\n    dplyr::filter(Anno2 %in% loopList,\n                  updown_A485_DMSO %in% c(\\UP\\, \\NO\\,  \\DOWN\\))\n  p <- ggplot(temp, aes(x = updown_A485_DMSO, y = distance)) +\n    geom_violin(aes(fill = updown_A485_DMSO)) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = updown_A485_DMSO), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + ggtitle(note) +\n    scale_y_continuous(labels = label_kb_mb)\n  fileName <- paste0(\\size_barplot_\\, name, \\_A485_vs_DMSO_\\, note, \\_\\, diffCutoff)\n  height <- 3\n  width <- 4\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n\nname\ndiffCutoff <- 0.2\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ndata <- data %>%\n  dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                distance = start2 - start1)\ndata$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ndata$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ncreate_dist_barplot(data, figDir, name, \\p-n\\, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\), 0.2)\ncreate_dist_barplot(data, figDir, name, \\p-pe\\, c(\\P-P\\, \\P-E\\), 0.2)\ncreate_dist_barplot(data, figDir, name, \\pe-pe\\, c(\\P-P\\, \\P-E\\, \\E-E\\), 0.2)\ncreate_dist_barplot(data, figDir, name, \\str\\, c(\\S-S\\, \\S-X\\), 0.2)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
create_dist_barplot <- function(data, figDir, name, note, loopList, diffCutoff){
temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%
dplyr::filter(Anno2 %in% loopList,
updown_dTAG_DMSO %in% c(\UP\, \NO\, \DOWN\))
p <- ggplot(temp, aes(x = updown_dTAG_DMSO, y = distance)) +
geom_violin(aes(fill = updown_dTAG_DMSO)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = updown_dTAG_DMSO), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + ggtitle(note) +
scale_y_continuous(labels = label_kb_mb)
fileName <- paste0(\size_barplot_\, name, \_dTAG_vs_DMSO_\, note, \_\, diffCutoff)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%
dplyr::filter(Anno2 %in% loopList,
updown_A485_DMSO %in% c(\UP\, \NO\, \DOWN\))
p <- ggplot(temp, aes(x = updown_A485_DMSO, y = distance)) +
geom_violin(aes(fill = updown_A485_DMSO)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = updown_A485_DMSO), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + ggtitle(note) +
scale_y_continuous(labels = label_kb_mb)
fileName <- paste0(\size_barplot_\, name, \_A485_vs_DMSO_\, note, \_\, diffCutoff)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
name
diffCutoff <- 0.2
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)),
distance = start2 - start1)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
create_dist_barplot(data, figDir, name, \p-n\, c(\P-P\, \P-E\, \P-S\, \P-X\), 0.2)
create_dist_barplot(data, figDir, name, \p-pe\, c(\P-P\, \P-E\), 0.2)
create_dist_barplot(data, figDir, name, \pe-pe\, c(\P-P\, \P-E\, \E-E\), 0.2)
create_dist_barplot(data, figDir, name, \str\, c(\S-S\, \S-X\), 0.2)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ncreate_dist_barplot <- function(data, figDir, name, note, loopList, diffCutoff){\n  temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%\n    dplyr::filter(Anno2 %in% loopList,\n                  updown_dTAG_DMSO %in% c(\\UP\\, \\NO\\, \\DOWN\\))\n  p <- ggplot(temp, aes(x = updown_dTAG_DMSO, y = distance)) +\n    geom_violin(aes(fill = updown_dTAG_DMSO)) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = updown_dTAG_DMSO), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + ggtitle(note) +\n    scale_y_continuous(labels = label_kb_mb)\n  fileName <- paste0(\\size_barplot_\\, name, \\_dTAG_vs_DMSO_\\, note, \\_\\, diffCutoff)\n  height <- 3\n  width <- 4\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n  \n  \n  temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%\n    dplyr::filter(Anno2 %in% loopList,\n                  updown_A485_DMSO %in% c(\\UP\\, \\NO\\,  \\DOWN\\))\n  p <- ggplot(temp, aes(x = updown_A485_DMSO, y = distance)) +\n    geom_violin(aes(fill = updown_A485_DMSO)) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = updown_A485_DMSO), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + ggtitle(note) +\n    scale_y_continuous(labels = label_kb_mb)\n  fileName <- paste0(\\size_barplot_\\, name, \\_A485_vs_DMSO_\\, note, \\_\\, diffCutoff)\n  height <- 3\n  width <- 4\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n\nname\ndiffCutoff <- 0.2\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ndata <- data %>%\n  dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                          ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                distance = start2 - start1)\ndata$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ndata$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\\UP\\, \\NO\\, \\DOWN\\))\ncreate_dist_barplot(data, figDir, name, \\p-n\\, c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\), 0.2)\ncreate_dist_barplot(data, figDir, name, \\p-pe\\, c(\\P-P\\, \\P-E\\), 0.2)\ncreate_dist_barplot(data, figDir, name, \\pe-pe\\, c(\\P-P\\, \\P-E\\, \\E-E\\), 0.2)\ncreate_dist_barplot(data, figDir, name, \\str\\, c(\\S-S\\, \\S-X\\), 0.2)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
create_dist_barplot <- function(data, figDir, name, note, loopList, diffCutoff){
temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%
dplyr::filter(Anno2 %in% loopList,
updown_dTAG_DMSO %in% c(\UP\, \NO\, \DOWN\))
p <- ggplot(temp, aes(x = updown_dTAG_DMSO, y = distance)) +
geom_violin(aes(fill = updown_dTAG_DMSO)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = updown_dTAG_DMSO), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + ggtitle(note) +
scale_y_continuous(labels = label_kb_mb)
fileName <- paste0(\size_barplot_\, name, \_dTAG_vs_DMSO_\, note, \_\, diffCutoff)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%
dplyr::filter(Anno2 %in% loopList,
updown_A485_DMSO %in% c(\UP\, \NO\, \DOWN\))
p <- ggplot(temp, aes(x = updown_A485_DMSO, y = distance)) +
geom_violin(aes(fill = updown_A485_DMSO)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = updown_A485_DMSO), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + ggtitle(note) +
scale_y_continuous(labels = label_kb_mb)
fileName <- paste0(\size_barplot_\, name, \_A485_vs_DMSO_\, note, \_\, diffCutoff)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
name
diffCutoff <- 0.2
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)),
distance = start2 - start1)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
create_dist_barplot(data, figDir, name, \p-n\, c(\P-P\, \P-E\, \P-S\, \P-X\), 0.2)
create_dist_barplot(data, figDir, name, \p-pe\, c(\P-P\, \P-E\), 0.2)
create_dist_barplot(data, figDir, name, \pe-pe\, c(\P-P\, \P-E\, \E-E\), 0.2)
create_dist_barplot(data, figDir, name, \str\, c(\S-S\, \S-X\), 0.2)
```
```
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<!-- rnb-output-begin 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-->
GOdir <- here(\../..\, \result\, \loop\, \GO\)
dir.create(GOdir, showWarnings = FALSE, recursive = TRUE)
getGO <- function(name, figDir, geneList, categoryNum = 15, height = 10, width = 7){
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
fwrite(as.data.frame(GO), here(GOdir, paste0(\GO_\, name, \_ensembl.tsv\)), sep = \\t\)
fwrite(as.data.frame(GO.readable), here(GOdir, paste0(\GO_\, name, \_readable.tsv\)), sep = \\t\)
if(nrow(as.data.frame(GO)) != 0){
#####
fileName <- paste0(\GO_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(dotplot(GO, showCategory = categoryNum, title = name) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
dev.off()
png(here(figDir, paste0(fileName, \.png\)), height = height, width = width, res = 600, unit = \in\)
print(dotplot(GO, showCategory = categoryNum, title = name) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
dev.off()
}
}
convPvalue <- function(pvalue){
out <- ifelse(pvalue < 0.0001, \****\,
ifelse(pvalue < 0.001, \***\,
ifelse(pvalue < 0.01, \**\,
ifelse(pvalue < 0.05, \*\, \ns\))))
return(out)
}
loadLoopAnnoData <- function(fileName, diffCutoff = 0.2, annoList = c(\P-P\, \P-E\, \P-S\, \P-X\)){
data <- fread(fileName) %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)),
gene = strsplit(gene, '\\|')) %>%
dplyr::filter(Anno2 %in% annoList)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
return(data)
}
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
GOdir <- here(\../..\, \result\, \loop\, \GO\)
dir.create(GOdir, showWarnings = FALSE, recursive = TRUE)
getGO <- function(name, figDir, geneList, categoryNum = 15, height = 10, width = 7){
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
fwrite(as.data.frame(GO), here(GOdir, paste0(\GO_\, name, \_ensembl.tsv\)), sep = \\t\)
fwrite(as.data.frame(GO.readable), here(GOdir, paste0(\GO_\, name, \_readable.tsv\)), sep = \\t\)
if(nrow(as.data.frame(GO)) != 0){
#####
fileName <- paste0(\GO_\, name)
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(dotplot(GO, showCategory = categoryNum, title = name) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
dev.off()
png(here(figDir, paste0(fileName, \.png\)), height = height, width = width, res = 600, unit = \in\)
print(dotplot(GO, showCategory = categoryNum, title = name) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
dev.off()
}
}
convPvalue <- function(pvalue){
out <- ifelse(pvalue < 0.0001, \****\,
ifelse(pvalue < 0.001, \***\,
ifelse(pvalue < 0.01, \**\,
ifelse(pvalue < 0.05, \*\, \ns\))))
return(out)
}
loadLoopAnnoData <- function(fileName, diffCutoff = 0.2, annoList = c(\P-P\, \P-E\, \P-S\, \P-X\)){
data <- fread(fileName) %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)),
gene = strsplit(gene, '\\|')) %>%
dplyr::filter(Anno2 %in% annoList)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
return(data)
}
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
### [2.9] Size distribution of diff loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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= \"dashed\") +\n#   geom_hline(yintercept = - diffCutoff, linetype = \"dashed\") +\n#   geom_vline(xintercept = 0) +\n#   geom_vline(xintercept = fcCutoff, linetype = \"dashed\") +\n#   geom_vline(xintercept = -fcCutoff, linetype = \"dashed\") +\n#   scale_color_manual(values = c(\"0NO\" = \"grey\", \"1UP\" = \"red\", \"2DOWN\" = \"blue\")) +  # Corrected color mapping\n#   scale_shape_manual(values = c(\"TRUE\" = 2, \"FALSE\" = 19))\n#\n# fileName <- paste0(\"log2FC_vs_avgScore_dTAG_\", loopType, \"_diffCutoff_\", diffCutoff)\n# height <- 4\n# width <- 7\n# png(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\n# print(p)\n# dev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\"gene\" = \"ensembl_gene_id\")) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \"2DOWN\",\n                              ifelse(gene %in% c(), \"1UP\", \"0NO\")),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n# fwrite(temp, here(consensusDir, paste0(\"gene_avgScore_fc_dTAG_\", loopType, \"_noFCcutoff.tsv\")), sep = \"\\t\")\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \"0NO\", external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point(size = 1, alpha = 1,\n      stroke = 0) + geom_text_repel() + theme_classic() + \n  guides(color = \"none\", shape = \"none\") + \n  geom_hline(yintercept = 0, alpha = 1,\n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\") +\n  geom_hline(yintercept = c(diffCutoff, -diffCutoff), linetype = \"dashed\", alpha = 1,\n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\") +\n  geom_vline(xintercept = 0, alpha = 1,\n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\") +\n  geom_vline(xintercept = c(fcCutoff, -fcCutoff), linetype = \"dashed\", alpha = 1,\n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\") +\n  theme(\n    axis.title.x = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),    \n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) + labs(y = \"Avg. Δ loop score\", x = \"log2(shrunken FC)\") +\n  scale_color_manual(values = c(\"0NO\" = \"#A9A8A9\", \"1UP\" = \"#CB333A\", \"2DOWN\" = \"#4852A0\")) +  # Corrected color mapping\n  scale_shape_manual(values = c(\"TRUE\" = 2, \"FALSE\" = 19))\n\nfileName <- paste0(\"log2FC_vs_avgScore_dTAG_\", loopType, \"_diffCutoff_\", diffCutoff, \"_noFCcutoff\")\nheight <- 42*mmToInch\nwidth <- 50*mmToInch\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - up and down\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\"gene\" = \"ensembl_gene_id\")) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.updown.RNA.noFCcutoff, \"2DOWN\",\n                              ifelse(gene %in% c(), \"1UP\", \"0NO\")),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \"0NO\", external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\"dTAG\") +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \"dashed\") +\n  geom_hline(yintercept = - diffCutoff, linetype = \"dashed\") + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \"dashed\") +\n  geom_vline(xintercept = -fcCutoff, linetype = \"dashed\") +\n  scale_color_manual(values = c(\"0NO\" = \"grey\", \"1UP\" = \"red\", \"2DOWN\" = \"blue\")) +  # Corrected color mapping\n  scale_shape_manual(values = c(\"TRUE\" = 2, \"FALSE\" = 19))\n\nfileName <- paste0(\"log2FC_vs_avgScore_dTAG_\", loopType, \"_diffCutoff_\", diffCutoff, \"_noFCcutoffupdown\")\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()\n\n# \n# ## RNA + PRO-seq\n# temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n#   unnest(gene) %>% group_by(gene) %>%\n#   summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n# \n# temp <- left_join(temp, diff.RNA, by = c(\"gene\" = \"ensembl_gene_id\")) %>% \n#   dplyr::mutate(flag = ifelse(gene %in% geneList.down, \"2DOWN\",\n#                               ifelse(gene %in% geneList.up, \"1UP\", \"0NO\")),\n#                 maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n#                 shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n#   dplyr::arrange(flag)\n# \n# fwrite(temp, here(consensusDir, \"gene_avgScore_fc_dTAG_\", loopType, \".tsv\"), sep = \"\\t\")\n# \n# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n#                       label = ifelse(flag != \"0NO\", external_gene_name, NA),\n#                       shape = as.factor(maxFlag))) +\n#   geom_point() + geom_text_repel() + theme_classic() + \n#   ggtitle(\"dTAG\") +\n#   geom_hline(yintercept = 0) +\n#   geom_hline(yintercept = diffCutoff, linetype = \"dashed\") +\n#   geom_hline(yintercept = - diffCutoff, linetype = \"dashed\") + \n#   geom_vline(xintercept = 0) +\n#   geom_vline(xintercept = fcCutoff, linetype = \"dashed\") +\n#   geom_vline(xintercept = -fcCutoff, linetype = \"dashed\") +\n#   scale_color_manual(values = c(\"0NO\" = \"grey\", \"1UP\" = \"red\", \"2DOWN\" = \"blue\")) +  # Corrected color mapping\n#   scale_shape_manual(values = c(\"TRUE\" = 2, \"FALSE\" = 19))\n# \n# fileName <- paste0(\"log2FC_vs_avgScore_dTAG_\", loopType, \"_diffCutoff_\", diffCutoff, \"_RNA-PRO\")\n# height <- 4\n# width <- 7\n# png(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\n# print(p)\n# dev.off()\n```"} -->\n\n```r\nloopType <- \\p-n\\\n\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>%\n  dplyr::mutate(distance = start2 - start1)\n# geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n#                                       diffCutoff = diffCutoff,\n#                                       annoList = c(\\P-P\\))%>% \n#   dplyr::mutate(distance = start2 - start1)\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\n#\n# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n#                       label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n#                       shape = as.factor(maxFlag))) +\n#   geom_point() + geom_text_repel() + theme_classic() +\n#   ggtitle(\\dTAG\\) +\n#   geom_hline(yintercept = 0) +\n#   geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n#   geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = 0) +\n#   geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n#   scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n#   scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n#\n# fileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff)\n# height <- 4\n# width <- 7\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n# fwrite(temp, here(consensusDir, paste0(\\gene_avgScore_fc_dTAG_\\, loopType, \\_noFCcutoff.tsv\\)), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point(size = 1, alpha = 1,\n      stroke = 0) + geom_text_repel() + theme_classic() + \n  guides(color = \\none\\, shape = \\none\\) + \n  geom_hline(yintercept = 0, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_hline(yintercept = c(diffCutoff, -diffCutoff), linetype = \\dashed\\, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_vline(xintercept = 0, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_vline(xintercept = c(fcCutoff, -fcCutoff), linetype = \\dashed\\, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  theme(\n    axis.title.x = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) + labs(y = \\Avg. Δ loop score\\, x = \\log2(shrunken FC)\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\#A9A8A9\\, \\1UP\\ = \\#CB333A\\, \\2DOWN\\ = \\#4852A0\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_noFCcutoff\\)\nheight <- 42*mmToInch\nwidth <- 50*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - up and down\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.updown.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_noFCcutoffupdown\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# \n# ## RNA + PRO-seq\n# temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n#   unnest(gene) %>% group_by(gene) %>%\n#   summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n# \n# temp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n#   dplyr::mutate(flag = ifelse(gene %in% geneList.down, \\2DOWN\\,\n#                               ifelse(gene %in% geneList.up, \\1UP\\, \\0NO\\)),\n#                 maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n#                 shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n#   dplyr::arrange(flag)\n# \n# fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_\\, loopType, \\.tsv\\), sep = \\\\t\\)\n# \n# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n#                       label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n#                       shape = as.factor(maxFlag))) +\n#   geom_point() + geom_text_repel() + theme_classic() + \n#   ggtitle(\\dTAG\\) +\n#   geom_hline(yintercept = 0) +\n#   geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n#   geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n#   geom_vline(xintercept = 0) +\n#   geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n#   scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n#   scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n# \n# fileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_RNA-PRO\\)\n# height <- 4\n# width <- 7\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
loopType <- \p-n\
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
# diffCutoff = diffCutoff,
# annoList = c(\P-P\))%>%
# dplyr::mutate(distance = start2 - start1)
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
#
# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
# label = ifelse(flag != \0NO\, external_gene_name, NA),
# shape = as.factor(maxFlag))) +
# geom_point() + geom_text_repel() + theme_classic() +
# ggtitle(\dTAG\) +
# geom_hline(yintercept = 0) +
# geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
# geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
# geom_vline(xintercept = 0) +
# geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
# geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
# scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
# scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
#
# fileName <- paste0(\log2FC_vs_avgScore_dTAG_\, loopType, \_diffCutoff_\, diffCutoff)
# height <- 4
# width <- 7
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
# fwrite(temp, here(consensusDir, paste0(\gene_avgScore_fc_dTAG_\, loopType, \_noFCcutoff.tsv\)), sep = \\t\)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point(size = 1, alpha = 1,
stroke = 0) + geom_text_repel() + theme_classic() +
guides(color = \none\, shape = \none\) +
geom_hline(yintercept = 0, alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = c(diffCutoff, -diffCutoff), linetype = \dashed\, alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = 0, alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = c(fcCutoff, -fcCutoff), linetype = \dashed\, alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
theme(
axis.title.x = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + labs(y = \Avg. Δ loop score\, x = \log2(shrunken FC)\) +
scale_color_manual(values = c(\0NO\ = \#A9A8A9\, \1UP\ = \#CB333A\, \2DOWN\ = \#4852A0\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_\, loopType, \_diffCutoff_\, diffCutoff, \_noFCcutoff\)
height <- 42*mmToInch
width <- 50*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - up and down
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.updown.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_\, loopType, \_diffCutoff_\, diffCutoff, \_noFCcutoffupdown\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#
# ## RNA + PRO-seq
# temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>%
# unnest(gene) %>% group_by(gene) %>%
# summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')
#
# temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
# dplyr::mutate(flag = ifelse(gene %in% geneList.down, \2DOWN\,
# ifelse(gene %in% geneList.up, \1UP\, \0NO\)),
# maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
# shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
# dplyr::arrange(flag)
#
# fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_\, loopType, \.tsv\), sep = \\t\)
#
# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
# label = ifelse(flag != \0NO\, external_gene_name, NA),
# shape = as.factor(maxFlag))) +
# geom_point() + geom_text_repel() + theme_classic() +
# ggtitle(\dTAG\) +
# geom_hline(yintercept = 0) +
# geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
# geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
# geom_vline(xintercept = 0) +
# geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
# geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
# scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
# scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
#
# fileName <- paste0(\log2FC_vs_avgScore_dTAG_\, loopType, \_diffCutoff_\, diffCutoff, \_RNA-PRO\)
# height <- 4
# width <- 7
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
<!-- rnb-output-end -->
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 -->\n\n```r\n```r\nloopType <- \\p-n\\\n\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>%\n  dplyr::mutate(distance = start2 - start1)\n# geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n#                                       diffCutoff = diffCutoff,\n#                                       annoList = c(\\P-P\\))%>% \n#   dplyr::mutate(distance = start2 - start1)\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\n#\n# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n#                       label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n#                       shape = as.factor(maxFlag))) +\n#   geom_point() + geom_text_repel() + theme_classic() +\n#   ggtitle(\\dTAG\\) +\n#   geom_hline(yintercept = 0) +\n#   geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n#   geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = 0) +\n#   geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n#   scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n#   scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n#\n# fileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff)\n# height <- 4\n# width <- 7\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n# fwrite(temp, here(consensusDir, paste0(\\gene_avgScore_fc_dTAG_\\, loopType, \\_noFCcutoff.tsv\\)), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point(size = 1, alpha = 1,\n      stroke = 0) + geom_text_repel() + theme_classic() + \n  guides(color = \\none\\, shape = \\none\\) + \n  geom_hline(yintercept = 0, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_hline(yintercept = c(diffCutoff, -diffCutoff), linetype = \\dashed\\, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_vline(xintercept = 0, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_vline(xintercept = c(fcCutoff, -fcCutoff), linetype = \\dashed\\, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  theme(\n    axis.title.x = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) + labs(y = \\Avg. Δ loop score\\, x = \\log2(shrunken FC)\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\#A9A8A9\\, \\1UP\\ = \\#CB333A\\, \\2DOWN\\ = \\#4852A0\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_noFCcutoff\\)\nheight <- 42*mmToInch\nwidth <- 50*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - up and down\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.updown.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_noFCcutoffupdown\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# \n# ## RNA + PRO-seq\n# temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n#   unnest(gene) %>% group_by(gene) %>%\n#   summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n# \n# temp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n#   dplyr::mutate(flag = ifelse(gene %in% geneList.down, \\2DOWN\\,\n#                               ifelse(gene %in% geneList.up, \\1UP\\, \\0NO\\)),\n#                 maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n#                 shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n#   dplyr::arrange(flag)\n# \n# fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_\\, loopType, \\.tsv\\), sep = \\\\t\\)\n# \n# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n#                       label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n#                       shape = as.factor(maxFlag))) +\n#   geom_point() + geom_text_repel() + theme_classic() + \n#   ggtitle(\\dTAG\\) +\n#   geom_hline(yintercept = 0) +\n#   geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n#   geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n#   geom_vline(xintercept = 0) +\n#   geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n#   scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n#   scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n# \n# fileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_RNA-PRO\\)\n# height <- 4\n# width <- 7\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nloopType <- \\p-n\\\n\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>%\n  dplyr::mutate(distance = start2 - start1)\n# geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n#                                       diffCutoff = diffCutoff,\n#                                       annoList = c(\\P-P\\))%>% \n#   dplyr::mutate(distance = start2 - start1)\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\n#\n# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,\n#                       label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n#                       shape = as.factor(maxFlag))) +\n#   geom_point() + geom_text_repel() + theme_classic() +\n#   ggtitle(\\dTAG\\) +\n#   geom_hline(yintercept = 0) +\n#   geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n#   geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = 0) +\n#   geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n#   scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n#   scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n#\n# fileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff)\n# height <- 4\n# width <- 7\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n# fwrite(temp, here(consensusDir, paste0(\\gene_avgScore_fc_dTAG_\\, loopType, \\_noFCcutoff.tsv\\)), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point(size = 1, alpha = 1,\n      stroke = 0) + geom_text_repel() + theme_classic() + \n  guides(color = \\none\\, shape = \\none\\) + \n  geom_hline(yintercept = 0, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_hline(yintercept = c(diffCutoff, -diffCutoff), linetype = \\dashed\\, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_vline(xintercept = 0, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_vline(xintercept = c(fcCutoff, -fcCutoff), linetype = \\dashed\\, alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  theme(\n    axis.title.x = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) + labs(y = \\Avg. Δ loop score\\, x = \\log2(shrunken FC)\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\#A9A8A9\\, \\1UP\\ = \\#CB333A\\, \\2DOWN\\ = \\#4852A0\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_noFCcutoff\\)\nheight <- 42*mmToInch\nwidth <- 50*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - up and down\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.updown.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_noFCcutoffupdown\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# \n# ## RNA + PRO-seq\n# temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>% \n#   unnest(gene) %>% group_by(gene) %>%\n#   summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')\n# \n# temp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n#   dplyr::mutate(flag = ifelse(gene %in% geneList.down, \\2DOWN\\,\n#                               ifelse(gene %in% geneList.up, \\1UP\\, \\0NO\\)),\n#                 maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n#                 shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n#   dplyr::arrange(flag)\n# \n# fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_\\, loopType, \\.tsv\\), sep = \\\\t\\)\n# \n# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n#                       label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n#                       shape = as.factor(maxFlag))) +\n#   geom_point() + geom_text_repel() + theme_classic() + \n#   ggtitle(\\dTAG\\) +\n#   geom_hline(yintercept = 0) +\n#   geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n#   geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n#   geom_vline(xintercept = 0) +\n#   geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n#   geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n#   scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n#   scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n# \n# fileName <- paste0(\\log2FC_vs_avgScore_dTAG_\\, loopType, \\_diffCutoff_\\, diffCutoff, \\_RNA-PRO\\)\n# height <- 4\n# width <- 7\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n```\n```"} -->
```r
```r
loopType <- \p-n\
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
# diffCutoff = diffCutoff,
# annoList = c(\P-P\))%>%
# dplyr::mutate(distance = start2 - start1)
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
#
# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
# label = ifelse(flag != \0NO\, external_gene_name, NA),
# shape = as.factor(maxFlag))) +
# geom_point() + geom_text_repel() + theme_classic() +
# ggtitle(\dTAG\) +
# geom_hline(yintercept = 0) +
# geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
# geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
# geom_vline(xintercept = 0) +
# geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
# geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
# scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
# scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
#
# fileName <- paste0(\log2FC_vs_avgScore_dTAG_\, loopType, \_diffCutoff_\, diffCutoff)
# height <- 4
# width <- 7
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
# fwrite(temp, here(consensusDir, paste0(\gene_avgScore_fc_dTAG_\, loopType, \_noFCcutoff.tsv\)), sep = \\t\)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point(size = 1, alpha = 1,
stroke = 0) + geom_text_repel() + theme_classic() +
guides(color = \none\, shape = \none\) +
geom_hline(yintercept = 0, alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = c(diffCutoff, -diffCutoff), linetype = \dashed\, alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = 0, alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_vline(xintercept = c(fcCutoff, -fcCutoff), linetype = \dashed\, alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
theme(
axis.title.x = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + labs(y = \Avg. Δ loop score\, x = \log2(shrunken FC)\) +
scale_color_manual(values = c(\0NO\ = \#A9A8A9\, \1UP\ = \#CB333A\, \2DOWN\ = \#4852A0\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_\, loopType, \_diffCutoff_\, diffCutoff, \_noFCcutoff\)
height <- 42*mmToInch
width <- 50*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - up and down
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.updown.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_\, loopType, \_diffCutoff_\, diffCutoff, \_noFCcutoffupdown\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#
# ## RNA + PRO-seq
# temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, gene) %>%
# unnest(gene) %>% group_by(gene) %>%
# summarize(mean_diff_score = mean(diff_dTAG_DMSO), .groups = 'drop')
#
# temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
# dplyr::mutate(flag = ifelse(gene %in% geneList.down, \2DOWN\,
# ifelse(gene %in% geneList.up, \1UP\, \0NO\)),
# maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
# shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
# dplyr::arrange(flag)
#
# fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_\, loopType, \.tsv\), sep = \\t\)
#
# p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
# label = ifelse(flag != \0NO\, external_gene_name, NA),
# shape = as.factor(maxFlag))) +
# geom_point() + geom_text_repel() + theme_classic() +
# ggtitle(\dTAG\) +
# geom_hline(yintercept = 0) +
# geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
# geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
# geom_vline(xintercept = 0) +
# geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
# geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
# scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
# scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
#
# fileName <- paste0(\log2FC_vs_avgScore_dTAG_\, loopType, \_diffCutoff_\, diffCutoff, \_RNA-PRO\)
# height <- 4
# width <- 7
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.10] Linking with RNA-seq/PRO-seq
#### Function & parameter
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n\nloopType <- \\p-pe\\\n\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_\\, loopType, \\_avgLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_\\, loopType, \\_avgLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
loopType <- \p-pe\
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_\, loopType, \_avgLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_\, loopType, \_avgLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n\nloopType <- \\p-pe\\\n\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_\\, loopType, \\_avgLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_\\, loopType, \\_avgLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n\nloopType <- \\p-pe\\\n\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_\\, loopType, \\_avgLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_\\, loopType, \\_avgLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
loopType <- \p-pe\
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_\, loopType, \_avgLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_\, loopType, \_avgLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### dTAG Scatterplot
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = sum(log_obsexp_diff_dTAG_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_sumLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### dTAG Scatterplot OE
##### Avg
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')\n\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')\n\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')\n\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO[which.max(abs(log_obsexp_diff_dTAG_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_absLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### Sum
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>% \n  slice_min(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  slice_min(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>%
unnest(gene) %>% group_by(gene) %>%
slice_min(distance, with_ties = FALSE) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>%
unnest(gene) %>% group_by(gene) %>%
slice_min(distance, with_ties = FALSE) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>% \n  slice_min(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  slice_min(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>% \n  slice_min(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  slice_min(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>%
unnest(gene) %>% group_by(gene) %>%
slice_min(distance, with_ties = FALSE) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>%
unnest(gene) %>% group_by(gene) %>%
slice_min(distance, with_ties = FALSE) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_closestLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### abs max
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>% \n  slice_max(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  slice_max(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>%
unnest(gene) %>% group_by(gene) %>%
slice_max(distance, with_ties = FALSE) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>%
unnest(gene) %>% group_by(gene) %>%
slice_max(distance, with_ties = FALSE) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>% \n  slice_max(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  slice_max(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\n# Loading OE loop score\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,\n                          (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))\ngeneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id\ngeneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id\n\ngeneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,\n                        (diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 2\n\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>% \n  slice_max(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n#fwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# No FC cutoff option - down only\ntemp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  slice_max(distance, with_ties = FALSE) %>% \n  summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \\2DOWN\\,\n                              ifelse(gene %in% c(), \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\n\np <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\dTAG\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\grey\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE_noFCcutoff\\)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Loading OE loop score
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id))
geneList.down.RNA <- (diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.down.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff %>% dplyr::filter(shrinked_log2FC < 0))$ensembl_gene_id
geneList.updown.RNA.noFCcutoff <- (diff.RNA.G1.dTAG.noFCcutoff)$ensembl_gene_id
geneList.up <- unique(c((diff.RNA.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G1.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id,
(diff.PRO.G2.dTAG %>% dplyr::filter(shrinked_log2FC > 0))$ensembl_gene_id))
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 2
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>%
unnest(gene) %>% group_by(gene) %>%
slice_max(distance, with_ties = FALSE) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
#fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# No FC cutoff option - down only
temp <- geneAnnoData %>% dplyr::select(log_obsexp_diff_dTAG_DMSO, gene, distance) %>%
unnest(gene) %>% group_by(gene) %>%
slice_max(distance, with_ties = FALSE) %>%
summarize(mean_diff_score = log_obsexp_diff_dTAG_DMSO, .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA.noFCcutoff, \2DOWN\,
ifelse(gene %in% c(), \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
p <- ggplot(temp, aes(x = shrinked_log2FC, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\dTAG\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \grey\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_dTAG_OE_p-n_farthestLogOE_noFCcutoff\)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### Closest P-N loop
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.G1.A485 <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_A485_p-n_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [2] Sum of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_sumScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_sumScore_A485_p-n_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [3] Max of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_maxAbsScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_maxAbsScore_A485_p-n_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-n.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-n_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n```\n```"} -->
```r
```r
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.G1.A485 <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_A485_p-n_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [2] Sum of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_sumScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_sumScore_A485_p-n_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [3] Max of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_maxAbsScore_fc_dTAG_p-n.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_maxAbsScore_A485_p-n_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### Farthest P-N loop
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.G1.A485 <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\))%>%
dplyr::mutate(distance = start2 - start1)
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-pe.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_A485_p-pe_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [2] Sum of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_sumScore_fc_dTAG_p-pe.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_sumScore_A485_p-pe_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [3] Max of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_maxAbsScore_fc_dTAG_p-pe.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_maxAbsScore_A485_p-pe_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-pe.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-pe_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.G1.A485 <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\))%>%
dplyr::mutate(distance = start2 - start1)
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-pe.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_A485_p-pe_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [2] Sum of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_sumScore_fc_dTAG_p-pe.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_sumScore_A485_p-pe_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [3] Max of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_maxAbsScore_fc_dTAG_p-pe.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_maxAbsScore_A485_p-pe_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### A485 Scatterplot
##### P-N
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-S\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.G1.A485 <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-S\))%>%
dplyr::mutate(distance = start2 - start1)
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-s.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_A485_p-s_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [2] Sum of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_sumScore_fc_dTAG_p-s.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_sumScore_A485_p-s_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [3] Max of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_maxAbsScore_fc_dTAG_p-s.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_maxAbsScore_A485_p-s_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-S\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# getting list of genes of interest from RNA-seq and PRO-seq\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.A485 <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\n\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-S\\))%>% \n  dplyr::mutate(distance = start2 - start1)\n\n# Testing different ways to calculate representative feature of loops for each gene\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ngeneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id\ngeneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id\n\n\n# [1] Average of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_avgScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_avgScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n# [2] Sum of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_sumScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_sumScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n# [3] Max of differential loop scores for each gene\n## RNA only\nfcCutoff <- 0.5\nalpha <- 0.05\nmaxLog2FC <- 4\n\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \\2DOWN\\,\n                              ifelse(gene %in% geneList.up.RNA, \\1UP\\, \\0NO\\)),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag)\n\nfwrite(temp, here(consensusDir, \\gene_maxAbsScore_fc_dTAG_p-s.tsv\\), sep = \\\\t\\)\n\np <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag, \n                      label = ifelse(flag != \\0NO\\, external_gene_name, NA),\n                      shape = as.factor(maxFlag))) +\n  geom_point() + geom_text_repel() + theme_classic() + \n  ggtitle(\\A485\\) +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = diffCutoff, linetype = \\dashed\\) +\n  geom_hline(yintercept = - diffCutoff, linetype = \\dashed\\) + \n  geom_vline(xintercept = 0) +\n  geom_vline(xintercept = fcCutoff, linetype = \\dashed\\) +\n  geom_vline(xintercept = -fcCutoff, linetype = \\dashed\\) +\n  scale_color_manual(values = c(\\0NO\\ = \\black\\, \\1UP\\ = \\red\\, \\2DOWN\\ = \\blue\\)) +  # Corrected color mapping\n  scale_shape_manual(values = c(\\TRUE\\ = 2, \\FALSE\\ = 19))\n\nfileName <- paste0(\\log2FC_vs_maxAbsScore_A485_p-s_diffCutoff_\\, diffCutoff)\nheight <- 4\nwidth <- 7\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
# getting list of genes of interest from RNA-seq and PRO-seq
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.G1.A485 <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-S\))%>%
dplyr::mutate(distance = start2 - start1)
# Testing different ways to calculate representative feature of loops for each gene
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
geneList.down.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC < -fcCutoff))$ensembl_gene_id
geneList.up.RNA <- (diff.RNA.G1.A485 %>% dplyr::filter(shrinked_log2FC > fcCutoff))$ensembl_gene_id
# [1] Average of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_avgScore_fc_dTAG_p-s.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = mean_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_avgScore_A485_p-s_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [2] Sum of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(sum_diff_score = sum(diff_A485_DMSO), .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_sumScore_fc_dTAG_p-s.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = sum_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_sumScore_A485_p-s_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
# [3] Max of differential loop scores for each gene
## RNA only
fcCutoff <- 0.5
alpha <- 0.05
maxLog2FC <- 4
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(max_abs_diff_score = diff_A485_DMSO[which.max(abs(diff_A485_DMSO))], .groups = 'drop')
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% geneList.down.RNA, \2DOWN\,
ifelse(gene %in% geneList.up.RNA, \1UP\, \0NO\)),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
shrlog2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag)
fwrite(temp, here(consensusDir, \gene_maxAbsScore_fc_dTAG_p-s.tsv\), sep = \\t\)
p <- ggplot(temp, aes(x = shrlog2fcMax, y = max_abs_diff_score, color = flag,
label = ifelse(flag != \0NO\, external_gene_name, NA),
shape = as.factor(maxFlag))) +
geom_point() + geom_text_repel() + theme_classic() +
ggtitle(\A485\) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = diffCutoff, linetype = \dashed\) +
geom_hline(yintercept = - diffCutoff, linetype = \dashed\) +
geom_vline(xintercept = 0) +
geom_vline(xintercept = fcCutoff, linetype = \dashed\) +
geom_vline(xintercept = -fcCutoff, linetype = \dashed\) +
scale_color_manual(values = c(\0NO\ = \black\, \1UP\ = \red\, \2DOWN\ = \blue\)) + # Corrected color mapping
scale_shape_manual(values = c(\TRUE\ = 2, \FALSE\ = 19))
fileName <- paste0(\log2FC_vs_maxAbsScore_A485_p-s_diffCutoff_\, diffCutoff)
height <- 4
width <- 7
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### P-PE
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# IMPORTING GENE ANNO DATA FOR P-N LOOPS
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
# Counting number of loop per genes
tempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>% summarize(
peak = list(peakID),
anno2 = list(Anno2),
count = n())
ggplot(tempSum, aes(x = count)) + geom_histogram(binwidth = 1) + theme_classic() +
ggtitle(\# of P-N loops for each gene\) + scale_y_log10()
###################################################################################
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```r
```r
# IMPORTING GENE ANNO DATA FOR P-N LOOPS
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
# Counting number of loop per genes
tempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>% summarize(
peak = list(peakID),
anno2 = list(Anno2),
count = n())
ggplot(tempSum, aes(x = count)) + geom_histogram(binwidth = 1) + theme_classic() +
ggtitle(\# of P-N loops for each gene\) + scale_y_log10()
###################################################################################
```
```
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##### P-S
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# IMPORTING GENE ANNO DATA FOR P-N LOOPS
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))
tempSE <- geneAnnoData %>% unnest(gene) %>% group_by(gene) %>% summarize(SE = ifelse(any(AnnoSE == \SE\), 1, 0))
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
tempSE <- tempSE %>% rowwise() %>% dplyr::mutate(
group = ifelse(gene %in% group1, \group1\,
ifelse(gene %in% group2, \group2\,
ifelse(gene %in% group5, \group5\,
ifelse(gene %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
perc.group1 <- nrow(tempSE %>% dplyr::filter(group == \group1\, SE == 1))/
nrow(tempSE %>% dplyr::filter(group == \group1\))
perc.group2 <- nrow(tempSE %>% dplyr::filter(group == \group2\, SE == 1))/
nrow(tempSE %>% dplyr::filter(group == \group2\))
perc.group5 <- nrow(tempSE %>% dplyr::filter(group == \group5\, SE == 1))/
nrow(tempSE %>% dplyr::filter(group == \group5\))
perc.group8 <- nrow(tempSE %>% dplyr::filter(group == \group8\, SE == 1))/
nrow(tempSE %>% dplyr::filter(group == \group8\))
tempPlot <- tibble(group = c(\group1\, \group2\, \group5\, \group8\),
perc = c(perc.group1, perc.group2, perc.group5, perc.group8))
ggplot(tempPlot, aes(x = group, y = perc)) + geom_point() + theme_classic() + ylim(0, 0.2)
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-->
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```r
```r
# IMPORTING GENE ANNO DATA FOR P-N LOOPS
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))
tempSE <- geneAnnoData %>% unnest(gene) %>% group_by(gene) %>% summarize(SE = ifelse(any(AnnoSE == \SE\), 1, 0))
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
tempSE <- tempSE %>% rowwise() %>% dplyr::mutate(
group = ifelse(gene %in% group1, \group1\,
ifelse(gene %in% group2, \group2\,
ifelse(gene %in% group5, \group5\,
ifelse(gene %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
perc.group1 <- nrow(tempSE %>% dplyr::filter(group == \group1\, SE == 1))/
nrow(tempSE %>% dplyr::filter(group == \group1\))
perc.group2 <- nrow(tempSE %>% dplyr::filter(group == \group2\, SE == 1))/
nrow(tempSE %>% dplyr::filter(group == \group2\))
perc.group5 <- nrow(tempSE %>% dplyr::filter(group == \group5\, SE == 1))/
nrow(tempSE %>% dplyr::filter(group == \group5\))
perc.group8 <- nrow(tempSE %>% dplyr::filter(group == \group8\, SE == 1))/
nrow(tempSE %>% dplyr::filter(group == \group8\))
tempPlot <- tibble(group = c(\group1\, \group2\, \group5\, \group8\),
perc = c(perc.group1, perc.group2, perc.group5, perc.group8))
ggplot(tempPlot, aes(x = group, y = perc)) + geom_point() + theme_classic() + ylim(0, 0.2)
```
```
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## [2.11] How many loops per gene?
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plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO\)
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```r
```r
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO\)
```
```
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## [2.11.2] SuperEnhancer?
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name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>%
dplyr::mutate(group = ifelse(padj < 0.05 & shrinked_log2FC < 0, 1,
ifelse(mean_diff_score < 0, 2, 3)))
fcCutoff <- 0.5
diffCutoff <- 0.2
gene.group1 <- (temp %>% dplyr::filter(group == 1))$gene
gene.group2 <- (temp %>% dplyr::filter(group == 2))$gene
fwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\), sep = \\t\)
# Adding group information to geneAnno
geneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\, NA))
)
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```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>%
dplyr::mutate(group = ifelse(padj < 0.05 & shrinked_log2FC < 0, 1,
ifelse(mean_diff_score < 0, 2, 3)))
fcCutoff <- 0.5
diffCutoff <- 0.2
gene.group1 <- (temp %>% dplyr::filter(group == 1))$gene
gene.group2 <- (temp %>% dplyr::filter(group == 2))$gene
fwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\), sep = \\t\)
# Adding group information to geneAnno
geneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\, NA))
)
```
```
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-->\n\n```r\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nGO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE)\nGO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE)\nGO1.df <- as.data.frame(GO1)\nGO2.df <- as.data.frame(GO2)\n# fwrite(GO1.df, here(\\GO_binary_group1.tsv\\), sep = \\\\t\\)\n# fwrite(GO2.df, here(\\GO_binary_group2.tsv\\), sep = \\\\t\\)\nGO1.df <- fread(here(\\GO_binary_group1.tsv\\))\nGO2.df <- fread(here(\\GO_binary_group2.tsv\\))\n\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\n\nGOlist <- factor(c(\\GO:0001824\\, \\GO:0030879\\, \\GO:0021953\\, \\GO:0019827\\, \n                 \\GO:0022613\\, \\GO:0050767\\, \\GO:0034470\\, \\GO:0016055\\, \n                 \\GO:0006397\\, \\GO:0030900\\, \\GO:0008380\\))\n\ndata <- bind_rows(subset1, subset2) %>%\n  dplyr::filter(ID %in% GOlist)\n\ndescOrder <- sort(unique(data$Description))[c(4, 10, 2, 1, 3, 11, 7, 8, 6, 5, 9)]\npValueLogMax <- 10\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n  \n\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_binaryGrouping\\)\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.2)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
GO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE)
GO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE)
GO1.df <- as.data.frame(GO1)
GO2.df <- as.data.frame(GO2)
# fwrite(GO1.df, here(\GO_binary_group1.tsv\), sep = \\t\)
# fwrite(GO2.df, here(\GO_binary_group2.tsv\), sep = \\t\)
GO1.df <- fread(here(\GO_binary_group1.tsv\))
GO2.df <- fread(here(\GO_binary_group2.tsv\))
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 1\) %>%
dplyr::arrange(p.adjust)
subset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 2\) %>%
dplyr::arrange(p.adjust)
subset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
GOlist <- factor(c(\GO:0001824\, \GO:0030879\, \GO:0021953\, \GO:0019827\,
\GO:0022613\, \GO:0050767\, \GO:0034470\, \GO:0016055\,
\GO:0006397\, \GO:0030900\, \GO:0008380\))
data <- bind_rows(subset1, subset2) %>%
dplyr::filter(ID %in% GOlist)
descOrder <- sort(unique(data$Description))[c(4, 10, 2, 1, 3, 11, 7, 8, 6, 5, 9)]
pValueLogMax <- 10
data <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))
p <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM) +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
# limits = c(0, 1),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme_bw() + # Apply theme_bw first, so custom theme settings come after
theme(
panel.background = element_rect(fill = \transparent\), # Override theme_bw panel
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeS, # Ensure size is set for x-axis text
family = fontType,
color = \#000000\,
),
axis.text.y = element_text(
size = fontSizeS, # Ensure size is set for y-axis text
family = fontType,
color = \#000000\,
lineheight = 0.9 # Allows wrapping for y-axis labels to fit into 2 lines
),
axis.line = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \..\, \GO\, \GO_groups_binaryGrouping\)
width <- panelSize(2.5)*mmToInch
height <- panelSize(1.2)*mmToInch
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nGO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE)\nGO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE)\nGO1.df <- as.data.frame(GO1)\nGO2.df <- as.data.frame(GO2)\n# fwrite(GO1.df, here(\\GO_binary_group1.tsv\\), sep = \\\\t\\)\n# fwrite(GO2.df, here(\\GO_binary_group2.tsv\\), sep = \\\\t\\)\nGO1.df <- fread(here(\\GO_binary_group1.tsv\\))\nGO2.df <- fread(here(\\GO_binary_group2.tsv\\))\n\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\n\nGOlist <- factor(c(\\GO:0001824\\, \\GO:0030879\\, \\GO:0021953\\, \\GO:0019827\\, \n                 \\GO:0022613\\, \\GO:0050767\\, \\GO:0034470\\, \\GO:0016055\\, \n                 \\GO:0006397\\, \\GO:0030900\\, \\GO:0008380\\))\n\ndata <- bind_rows(subset1, subset2) %>%\n  dplyr::filter(ID %in% GOlist)\n\ndescOrder <- sort(unique(data$Description))[c(4, 10, 2, 1, 3, 11, 7, 8, 6, 5, 9)]\npValueLogMax <- 10\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n  \n\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_binaryGrouping\\)\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.2)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5nZW5lLmdyb3VwMSA8LSBmcmVhZChoZXJlKHJlZkRpciwgXFxnZW5lTGlzdF9kVEFHX3ZzX0RNU09fUk5BX2xvb3BfYmluYXJ5R3JvdXAxLnRzdlxcKSkkZ2VuZVxuZ2VuZS5ncm91cDIgPC0gZnJlYWQoaGVyZShyZWZEaXIsIFxcZ2VuZUxpc3RfZFRBR192c19ETVNPX1JOQV9sb29wX2JpbmFyeUdyb3VwMi50c3ZcXCkpJGdlbmVcblxuR08xIDwtIGVucmljaEdPKGdlbmUgPSBnZW5lLmdyb3VwMSwgT3JnRGIgPSBvcmcuTW0uZWcuZGIsIGtleVR5cGUgPSBcXEVOU0VNQkxcXCwgb250ID0gXFxCUFxcLCByZWFkYWJsZSA9IFRSVUUpXG5HTzIgPC0gZW5yaWNoR08oZ2VuZSA9IGdlbmUuZ3JvdXAyLCBPcmdEYiA9IG9yZy5NbS5lZy5kYiwga2V5VHlwZSA9IFxcRU5TRU1CTFxcLCBvbnQgPSBcXEJQXFwsIHJlYWRhYmxlID0gVFJVRSlcbkdPMS5kZiA8LSBhcy5kYXRhLmZyYW1lKEdPMSlcbkdPMi5kZiA8LSBhcy5kYXRhLmZyYW1lKEdPMilcbiMgZndyaXRlKEdPMS5kZiwgaGVyZShcXEdPX2JpbmFyeV9ncm91cDEudHN2XFwpLCBzZXAgPSBcXFxcdFxcKVxuIyBmd3JpdGUoR08yLmRmLCBoZXJlKFxcR09fYmluYXJ5X2dyb3VwMi50c3ZcXCksIHNlcCA9IFxcXFx0XFwpXG5HTzEuZGYgPC0gZnJlYWQoaGVyZShcXEdPX2JpbmFyeV9ncm91cDEudHN2XFwpKVxuR08yLmRmIDwtIGZyZWFkKGhlcmUoXFxHT19iaW5hcnlfZ3JvdXAyLnRzdlxcKSlcblxuc3Vic2V0MSA8LSBHTzEuZGYgJT4lIGRwbHlyOjpzZWxlY3QoSUQsIERlc2NyaXB0aW9uLCBHZW5lUmF0aW8sIHAuYWRqdXN0KSAlPiUgZHBseXI6Om11dGF0ZShncm91cCA9IFxcR3JvdXAgMVxcKSAlPiVcbiAgZHBseXI6OmFycmFuZ2UocC5hZGp1c3QpXG5zdWJzZXQxJEdlbmVSYXRpbyA8LSBzYXBwbHkoc3Ryc3BsaXQoc3Vic2V0MSRHZW5lUmF0aW8sIFxcL1xcKSwgZnVuY3Rpb24oeCkgYXMubnVtZXJpYyh4WzFdKSAvIGFzLm51bWVyaWMoeFsyXSkpXG5cbnN1YnNldDIgPC0gR08yLmRmICU+JSBkcGx5cjo6c2VsZWN0KElELCBEZXNjcmlwdGlvbiwgR2VuZVJhdGlvLCBwLmFkanVzdCkgJT4lIGRwbHlyOjptdXRhdGUoZ3JvdXAgPSBcXEdyb3VwIDJcXCkgJT4lXG4gIGRwbHlyOjphcnJhbmdlKHAuYWRqdXN0KVxuc3Vic2V0MiRHZW5lUmF0aW8gPC0gc2FwcGx5KHN0cnNwbGl0KHN1YnNldDIkR2VuZVJhdGlvLCBcXC9cXCksIGZ1bmN0aW9uKHgpIGFzLm51bWVyaWMoeFsxXSkgLyBhcy5udW1lcmljKHhbMl0pKVxuXG5cbkdPbGlzdCA8LSBmYWN0b3IoYyhcXEdPOjAwMDE4MjRcXCwgXFxHTzowMDMwODc5XFwsIFxcR086MDAyMTk1M1xcLCBcXEdPOjAwMTk4MjdcXCwgXG4gICAgICAgICAgICAgICAgIFxcR086MDAyMjYxM1xcLCBcXEdPOjAwNTA3NjdcXCwgXFxHTzowMDM0NDcwXFwsIFxcR086MDAxNjA1NVxcLCBcbiAgICAgICAgICAgICAgICAgXFxHTzowMDA2Mzk3XFwsIFxcR086MDAzMDkwMFxcLCBcXEdPOjAwMDgzODBcXCkpXG5cbmRhdGEgPC0gYmluZF9yb3dzKHN1YnNldDEsIHN1YnNldDIpICU+JVxuICBkcGx5cjo6ZmlsdGVyKElEICVpbiUgR09saXN0KVxuXG5kZXNjT3JkZXIgPC0gc29ydCh1bmlxdWUoZGF0YSREZXNjcmlwdGlvbikpW2MoNCwgMTAsIDIsIDEsIDMsIDExLCA3LCA4LCA2LCA1LCA5KV1cbnBWYWx1ZUxvZ01heCA8LSAxMFxuZGF0YSA8LSBkYXRhICU+JSBkcGx5cjo6cm93d2lzZSgpICU+JSBkcGx5cjo6bXV0YXRlKHBWYWx1ZUxvZyA9IG1pbigtbG9nMTAocC5hZGp1c3QpLCBwVmFsdWVMb2dNYXgpKVxucCA8LSBnZ3Bsb3QoZGF0YSwgYWVzKHggPSBncm91cCwgeSA9IGZhY3RvcihEZXNjcmlwdGlvbiwgbGV2ZWxzID0gZGVzY09yZGVyKSwgc2l6ZSA9IHBWYWx1ZUxvZywgZmlsbCA9IEdlbmVSYXRpbykpICsgXG4gIGdlb21fcG9pbnQoc2hhcGUgPSAyMSwgICAgICAgICMgRW5zdXJlcyBhIHBvaW50IHdpdGggYW4gb3V0bGluZVxuICAgICAgICAgICAgIHN0cm9rZSA9IDEqcHRUb01NKSArIFxuICBzY2FsZV9zaXplX2NvbnRpbnVvdXMocmFuZ2UgPSBjKDAuNSwgMikpICsgICMgU2V0IG1pbiBhbmQgbWF4IHBvaW50IHNpemVzIGhlcmVcbiAgc2NhbGVfZmlsbF9ncmFkaWVudChsb3cgPSBcXHdoaXRlXFwsIGhpZ2ggPSBcXCNDQjMzM0FcXCxcbiAgICAgICAgICAgICAgICAgICAgICAjIGxpbWl0cyA9IGMoMCwgMSksXG4gICAgICAgICAgICAgICAgICAgICAgb29iID0gc2NhbGVzOjpzcXVpc2gsICMgRGVmaW5lIGdyYWRpZW50IGNvbG9yc1xuICAgICAgICAgICAgICAgICAgICAgIGd1aWRlID0gZ3VpZGVfY29sb3JiYXIoXG4gICAgICAgICAgICAgICAgICAgICAgICBiYXJ3aWR0aCA9IDEuNS81LjA4LCAgIyBBZGp1c3Qgd2lkdGggb2YgdGhlIGNvbG9yIGJhclxuICAgICAgICAgICAgICAgICAgICAgICAgYmFyaGVpZ2h0ID0gMTUvNS4wOCAgICMgQWRqdXN0IGhlaWdodCBvZiB0aGUgY29sb3IgYmFyXG4gICAgICAgICAgICAgICAgICAgICAgKVxuICApICsgXG4gIGxhYnMoeCA9IE5VTEwsIHkgPSBOVUxMKSArXG4gIHRoZW1lX2J3KCkgKyAgIyBBcHBseSB0aGVtZV9idyBmaXJzdCwgc28gY3VzdG9tIHRoZW1lIHNldHRpbmdzIGNvbWUgYWZ0ZXJcbiAgdGhlbWUoXG4gICAgcGFuZWwuYmFja2dyb3VuZCA9IGVsZW1lbnRfcmVjdChmaWxsID0gXFx0cmFuc3BhcmVudFxcKSwgICMgT3ZlcnJpZGUgdGhlbWVfYncgcGFuZWxcbiAgICBheGlzLnRpdGxlID0gZWxlbWVudF90ZXh0KFxuICAgICAgc2l6ZSA9IGZvbnRTaXplUyxcbiAgICAgIGZhbWlseSA9IGZvbnRUeXBlLFxuICAgICAgY29sb3IgPSBcXCMwMDAwMDBcXFxuICAgICksXG4gICAgYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoXG4gICAgICBzaXplID0gZm9udFNpemVTLCAgICAgICAgICMgRW5zdXJlIHNpemUgaXMgc2V0IGZvciB4LWF4aXMgdGV4dFxuICAgICAgZmFtaWx5ID0gZm9udFR5cGUsXG4gICAgICBjb2xvciA9IFxcIzAwMDAwMFxcLFxuICAgICksXG4gICAgYXhpcy50ZXh0LnkgPSBlbGVtZW50X3RleHQoXG4gICAgICBzaXplID0gZm9udFNpemVTLCAgICAgICAgICMgRW5zdXJlIHNpemUgaXMgc2V0IGZvciB5LWF4aXMgdGV4dFxuICAgICAgZmFtaWx5ID0gZm9udFR5cGUsXG4gICAgICBjb2xvciA9IFxcIzAwMDAwMFxcLFxuICAgICAgbGluZWhlaWdodCA9IDAuOSAgICAgICAgICAjIEFsbG93cyB3cmFwcGluZyBmb3IgeS1heGlzIGxhYmVscyB0byBmaXQgaW50byAyIGxpbmVzXG4gICAgKSxcbiAgICBheGlzLmxpbmUgPSBlbGVtZW50X2xpbmUoXG4gICAgICBjb2xvciA9IFxcIzAwMDAwMFxcLFxuICAgICAgc2l6ZSA9IGxpbmVUaGljayAqIG1tVG9MaW5lVW5pdCxcbiAgICAgIGxpbmVlbmQgPSBcXHNxdWFyZVxcXG4gICAgKSxcbiAgICBheGlzLnRpY2tzID0gZWxlbWVudF9saW5lKFxuICAgICAgY29sb3IgPSBcXCMwMDAwMDBcXCxcbiAgICAgIHNpemUgPSBsaW5lVGhpY2sgKiBtbVRvTGluZVVuaXQsXG4gICAgICBsaW5lZW5kID0gXFxzcXVhcmVcXFxuICAgICksXG4gICAgbGVnZW5kLnRleHQgPSBlbGVtZW50X3RleHQoZmFtaWx5ID0gZm9udFR5cGUsIHNpemUgPSBmb250U2l6ZVMpLFxuICAgIGxlZ2VuZC50aXRsZSA9IGVsZW1lbnRfdGV4dChmYW1pbHkgPSBmb250VHlwZSwgc2l6ZSA9IGZvbnRTaXplUylcbiAgKVxuICBcblxuXG5maWxlTmFtZSA8LSBoZXJlKGZpZ0RpciwgXFwuLlxcLCBcXEdPXFwsIFxcR09fZ3JvdXBzX2JpbmFyeUdyb3VwaW5nXFwpXG53aWR0aCA8LSBwYW5lbFNpemUoMi41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-->
```r
```r
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
GO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE)
GO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE)
GO1.df <- as.data.frame(GO1)
GO2.df <- as.data.frame(GO2)
# fwrite(GO1.df, here(\GO_binary_group1.tsv\), sep = \\t\)
# fwrite(GO2.df, here(\GO_binary_group2.tsv\), sep = \\t\)
GO1.df <- fread(here(\GO_binary_group1.tsv\))
GO2.df <- fread(here(\GO_binary_group2.tsv\))
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 1\) %>%
dplyr::arrange(p.adjust)
subset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 2\) %>%
dplyr::arrange(p.adjust)
subset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
GOlist <- factor(c(\GO:0001824\, \GO:0030879\, \GO:0021953\, \GO:0019827\,
\GO:0022613\, \GO:0050767\, \GO:0034470\, \GO:0016055\,
\GO:0006397\, \GO:0030900\, \GO:0008380\))
data <- bind_rows(subset1, subset2) %>%
dplyr::filter(ID %in% GOlist)
descOrder <- sort(unique(data$Description))[c(4, 10, 2, 1, 3, 11, 7, 8, 6, 5, 9)]
pValueLogMax <- 10
data <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))
p <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM) +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
# limits = c(0, 1),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme_bw() + # Apply theme_bw first, so custom theme settings come after
theme(
panel.background = element_rect(fill = \transparent\), # Override theme_bw panel
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeS, # Ensure size is set for x-axis text
family = fontType,
color = \#000000\,
),
axis.text.y = element_text(
size = fontSizeS, # Ensure size is set for y-axis text
family = fontType,
color = \#000000\,
lineheight = 0.9 # Allows wrapping for y-axis labels to fit into 2 lines
),
axis.line = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \..\, \GO\, \GO_groups_binaryGrouping\)
width <- panelSize(2.5)*mmToInch
height <- panelSize(1.2)*mmToInch
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.12] How many loops/distance per gene in each quadrant?
#### dTAG with binary grouping
##### Splitting genes to group
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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# Checking average distance of loops per gene
# temp is a tibble where delta loop and log2fc are merged
temp$group <- factor(temp$group)
temp <- temp %>% dplyr::filter(group %in% c(1, 2))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$mean_distance
distance2 <- (data %>% dplyr::filter(group ==group2) )$mean_distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(temp, 1, 2), 5)
p <- ggplot(temp, aes(x = group, y = mean_distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + scale_y_continuous(labels = label_kb_mb) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)+
annotate(\text\, x = 1, y = 1000000, label = paste0(\pv12: \, pv12),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\size_barplot_diffGroup_dTAG_vs_DMSO_binaryGroup\)
height <- 3
width <- 2
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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```r
```r
# Checking average distance of loops per gene
# temp is a tibble where delta loop and log2fc are merged
temp$group <- factor(temp$group)
temp <- temp %>% dplyr::filter(group %in% c(1, 2))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$mean_distance
distance2 <- (data %>% dplyr::filter(group ==group2) )$mean_distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(temp, 1, 2), 5)
p <- ggplot(temp, aes(x = group, y = mean_distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + scale_y_continuous(labels = label_kb_mb) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)+
annotate(\text\, x = 1, y = 1000000, label = paste0(\pv12: \, pv12),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\size_barplot_diffGroup_dTAG_vs_DMSO_binaryGroup\)
height <- 3
width <- 2
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### - GO for each group
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-->\n\n```r\n# Counting number of loop per genes\ntempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>%  summarize(\n    peak = list(peakID),\n    anno2 = list(Anno2),\n    count = n())\n\ntempSum <- tempSum %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\, NA))\n) %>% dplyr::filter(!is.na(group)) %>% dplyr::filter(group %in% c(\\group1\\, \\group2\\))\n\n# \ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\npv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\n# pv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\n# pv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\n# pv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\n# pv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\n# pv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(tempSum, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA, color = \\black\\, alpha = 0.6,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\, show.legend = FALSE) + theme_classic() +       \n  scale_y_continuous(breaks = seq(0, 10, by = 2)) + \n  labs(x = NULL, y = \\# of P-N loops per gene\\) +\n  coord_cartesian(ylim = c(0, 8)) +\n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  )+\n  annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, convPvalue(pv12)),\n           color = \\black\\, hjust = 0, size = 1) +\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\n\nfileName <- paste0(\\count_barplot_diffGroup_dTAG_vs_DMSO_binaryGroup\\)\nwidth <- panelSize(0.8)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# Counting number of loop per genes
tempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>% summarize(
peak = list(peakID),
anno2 = list(Anno2),
count = n())
tempSum <- tempSum %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\, NA))
) %>% dplyr::filter(!is.na(group)) %>% dplyr::filter(group %in% c(\group1\, \group2\))
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$count
distance2 <- (data %>% dplyr::filter(group ==group2) )$count
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(tempSum,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(tempSum,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(tempSum,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(tempSum,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(tempSum,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(tempSum,\group5\, \group8\), 5)
p <- ggplot(tempSum, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA, color = \black\, alpha = 0.6,
linewidth = lineThick * mmToLineUnit, lineend = \square\, show.legend = FALSE) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
labs(x = NULL, y = \# of P-N loops per gene\) +
coord_cartesian(ylim = c(0, 8)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
)+
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, convPvalue(pv12)),
color = \black\, hjust = 0, size = 1) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_binaryGroup\)
width <- panelSize(0.8)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# Counting number of loop per genes\ntempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>%  summarize(\n    peak = list(peakID),\n    anno2 = list(Anno2),\n    count = n())\n\ntempSum <- tempSum %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\, NA))\n) %>% dplyr::filter(!is.na(group)) %>% dplyr::filter(group %in% c(\\group1\\, \\group2\\))\n\n# \ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\npv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\n# pv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\n# pv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\n# pv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\n# pv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\n# pv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(tempSum, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA, color = \\black\\, alpha = 0.6,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\, show.legend = FALSE) + theme_classic() +       \n  scale_y_continuous(breaks = seq(0, 10, by = 2)) + \n  labs(x = NULL, y = \\# of P-N loops per gene\\) +\n  coord_cartesian(ylim = c(0, 8)) +\n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  )+\n  annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, convPvalue(pv12)),\n           color = \\black\\, hjust = 0, size = 1) +\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\n\nfileName <- paste0(\\count_barplot_diffGroup_dTAG_vs_DMSO_binaryGroup\\)\nwidth <- panelSize(0.8)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
# Counting number of loop per genes
tempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>% summarize(
peak = list(peakID),
anno2 = list(Anno2),
count = n())
tempSum <- tempSum %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\, NA))
) %>% dplyr::filter(!is.na(group)) %>% dplyr::filter(group %in% c(\group1\, \group2\))
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$count
distance2 <- (data %>% dplyr::filter(group ==group2) )$count
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(tempSum,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(tempSum,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(tempSum,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(tempSum,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(tempSum,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(tempSum,\group5\, \group8\), 5)
p <- ggplot(tempSum, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA, color = \black\, alpha = 0.6,
linewidth = lineThick * mmToLineUnit, lineend = \square\, show.legend = FALSE) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
labs(x = NULL, y = \# of P-N loops per gene\) +
coord_cartesian(ylim = c(0, 8)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
)+
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, convPvalue(pv12)),
color = \black\, hjust = 0, size = 1) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_binaryGroup\)
width <- panelSize(0.8)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Average loop size
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
#######
temp2 <- tempSum %>% rowwise() %>% mutate(total = length(anno2),
num_pp = sum(anno2 == \P-P\),
num_pe = sum(anno2 == \P-E\),
num_ps = sum(anno2 == \P-S\),
num_px = sum(anno2 == \P-X\),
ratio_reg = (num_pp + num_pe)/total,
ratio_str = num_ps/total)
#saveRDS(temp2, here(resultDir, \gene_loop_link_A485.rds\))
loopType <- temp2 %>% group_by(group) %>% summarise(num_pp = sum(num_pp),
num_pe = sum(num_pe),
num_ps = sum(num_ps),
num_px = sum(num_px))
loopTypeLong <- loopType %>% pivot_longer(-group, names_to = \type\, values_to = \count\)
loopTypeLong$type <- factor(loopTypeLong$type, levels = c(\num_pp\, \num_pe\, \num_ps\, \num_px\))
# Plotting
ggplot(loopTypeLong, aes(fill=type, y=count, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
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```r
```r
#######
temp2 <- tempSum %>% rowwise() %>% mutate(total = length(anno2),
num_pp = sum(anno2 == \P-P\),
num_pe = sum(anno2 == \P-E\),
num_ps = sum(anno2 == \P-S\),
num_px = sum(anno2 == \P-X\),
ratio_reg = (num_pp + num_pe)/total,
ratio_str = num_ps/total)
#saveRDS(temp2, here(resultDir, \gene_loop_link_A485.rds\))
loopType <- temp2 %>% group_by(group) %>% summarise(num_pp = sum(num_pp),
num_pe = sum(num_pe),
num_ps = sum(num_ps),
num_px = sum(num_px))
loopTypeLong <- loopType %>% pivot_longer(-group, names_to = \type\, values_to = \count\)
loopTypeLong$type <- factor(loopTypeLong$type, levels = c(\num_pp\, \num_pe\, \num_ps\, \num_px\))
# Plotting
ggplot(loopTypeLong, aes(fill=type, y=count, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Loop number per gene: P-N
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
########
#P-P
data <- temp2 %>% dplyr::select(group, num_pp)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA, color = \black\, alpha = 0.6,
linewidth = lineThick * mmToLineUnit, lineend = \square\, show.legend = FALSE) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
labs(x = NULL, y = \# of P-P loops per gene\) +
coord_cartesian(ylim = c(0, 4)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
)+
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, convPvalue(pv12)),
color = \black\, hjust = 0, size = 1) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_pp_binaryGroup\)
width <- panelSize(0.8)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
########
#P-P
data <- temp2 %>% dplyr::select(group, num_pp)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA, color = \black\, alpha = 0.6,
linewidth = lineThick * mmToLineUnit, lineend = \square\, show.legend = FALSE) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
labs(x = NULL, y = \# of P-P loops per gene\) +
coord_cartesian(ylim = c(0, 4)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
)+
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, convPvalue(pv12)),
color = \black\, hjust = 0, size = 1) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_pp_binaryGroup\)
width <- panelSize(0.8)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Counting loop types
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
########
#P-E
data <- temp2 %>% dplyr::select(group, num_pe)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA, color = \black\, alpha = 0.6,
linewidth = lineThick * mmToLineUnit, lineend = \square\, show.legend = FALSE) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
labs(x = NULL, y = \# of P-E loops per gene\) +
coord_cartesian(ylim = c(0, 4)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
)+
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, convPvalue(pv12)),
color = \black\, hjust = 0, size = 1) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_pe_binaryGroup\)
width <- panelSize(0.8)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
########
#P-E
data <- temp2 %>% dplyr::select(group, num_pe)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA, color = \black\, alpha = 0.6,
linewidth = lineThick * mmToLineUnit, lineend = \square\, show.legend = FALSE) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
labs(x = NULL, y = \# of P-E loops per gene\) +
coord_cartesian(ylim = c(0, 4)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
)+
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, convPvalue(pv12)),
color = \black\, hjust = 0, size = 1) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_pe_binaryGroup\)
width <- panelSize(0.8)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Loop number per gene: P-P
<!-- rnb-text-end -->
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########
#P-S
data <- temp2 %>% dplyr::select(group, num_ps)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA, color = \black\, alpha = 0.6,
linewidth = lineThick * mmToLineUnit, lineend = \square\, show.legend = FALSE) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
labs(x = NULL, y = \# of P-S loops per gene\) +
coord_cartesian(ylim = c(0, 4)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
)+
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, convPvalue(pv12)),
color = \black\, hjust = 0, size = 1) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_ps_binaryGroup\)
width <- panelSize(0.8)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->
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```r
```r
########
#P-S
data <- temp2 %>% dplyr::select(group, num_ps)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA, color = \black\, alpha = 0.6,
linewidth = lineThick * mmToLineUnit, lineend = \square\, show.legend = FALSE) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
labs(x = NULL, y = \# of P-S loops per gene\) +
coord_cartesian(ylim = c(0, 4)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
)+
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, convPvalue(pv12)),
color = \black\, hjust = 0, size = 1) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_ps_binaryGroup\)
width <- panelSize(0.8)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Loop number per gene: P-E
<!-- rnb-text-end -->
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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nmaxLog2FC = 2\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  drop_na(shrinked_log2FC)\n\n\nfcCutoff <- 0.5\ndiffCutoff <- 0.2\n# \ntemp <- temp %>%\n  dplyr::mutate(group = ifelse(mean_diff_score < -diffCutoff,\n                               ifelse(shrinked_log2FC < -fcCutoff, 1,\n                                      ifelse(shrinked_log2FC <fcCutoff, 2, 3)),\n                               ifelse(mean_diff_score < diffCutoff,\n                                      ifelse(shrinked_log2FC < -fcCutoff, 4,\n                                             ifelse(shrinked_log2FC < fcCutoff, 5, 6)),\n                                      ifelse(shrinked_log2FC < -fcCutoff, 7,\n                                             ifelse(shrinked_log2FC < fcCutoff, 8, 9)))))\n\n\ngene.group1 <- (temp %>% dplyr::filter(group == 1))$gene\ngene.group2 <- (temp %>% dplyr::filter(group == 2))$gene\ngene.group3 <- (temp %>% dplyr::filter(group == 3))$gene\ngene.group4 <- (temp %>% dplyr::filter(group == 4))$gene\ngene.group5 <- (temp %>% dplyr::filter(group == 5))$gene\ngene.group6 <- (temp %>% dplyr::filter(group == 6))$gene\ngene.group7 <- (temp %>% dplyr::filter(group == 7))$gene\ngene.group8 <- (temp %>% dplyr::filter(group == 8))$gene\ngene.group9 <- (temp %>% dplyr::filter(group == 9))$gene\n\nfwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 3)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group3.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 4)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group4.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 5)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 6)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group6.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 7)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group7.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 8)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 9)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group9.tsv\\), sep = \\\\t\\)\n\n\n# Adding group information to geneAnno\ngeneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
fcCutoff <- 0.5
diffCutoff <- 0.2
#
temp <- temp %>%
dplyr::mutate(group = ifelse(mean_diff_score < -diffCutoff,
ifelse(shrinked_log2FC < -fcCutoff, 1,
ifelse(shrinked_log2FC <fcCutoff, 2, 3)),
ifelse(mean_diff_score < diffCutoff,
ifelse(shrinked_log2FC < -fcCutoff, 4,
ifelse(shrinked_log2FC < fcCutoff, 5, 6)),
ifelse(shrinked_log2FC < -fcCutoff, 7,
ifelse(shrinked_log2FC < fcCutoff, 8, 9)))))
gene.group1 <- (temp %>% dplyr::filter(group == 1))$gene
gene.group2 <- (temp %>% dplyr::filter(group == 2))$gene
gene.group3 <- (temp %>% dplyr::filter(group == 3))$gene
gene.group4 <- (temp %>% dplyr::filter(group == 4))$gene
gene.group5 <- (temp %>% dplyr::filter(group == 5))$gene
gene.group6 <- (temp %>% dplyr::filter(group == 6))$gene
gene.group7 <- (temp %>% dplyr::filter(group == 7))$gene
gene.group8 <- (temp %>% dplyr::filter(group == 8))$gene
gene.group9 <- (temp %>% dplyr::filter(group == 9))$gene
fwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 3)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group3.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 4)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group4.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 5)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 6)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group6.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 7)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group7.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 8)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 9)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group9.tsv\), sep = \\t\)
# Adding group information to geneAnno
geneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
)
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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nmaxLog2FC = 2\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  drop_na(shrinked_log2FC)\n\n\nfcCutoff <- 0.5\ndiffCutoff <- 0.2\n# \ntemp <- temp %>%\n  dplyr::mutate(group = ifelse(mean_diff_score < -diffCutoff,\n                               ifelse(shrinked_log2FC < -fcCutoff, 1,\n                                      ifelse(shrinked_log2FC <fcCutoff, 2, 3)),\n                               ifelse(mean_diff_score < diffCutoff,\n                                      ifelse(shrinked_log2FC < -fcCutoff, 4,\n                                             ifelse(shrinked_log2FC < fcCutoff, 5, 6)),\n                                      ifelse(shrinked_log2FC < -fcCutoff, 7,\n                                             ifelse(shrinked_log2FC < fcCutoff, 8, 9)))))\n\n\ngene.group1 <- (temp %>% dplyr::filter(group == 1))$gene\ngene.group2 <- (temp %>% dplyr::filter(group == 2))$gene\ngene.group3 <- (temp %>% dplyr::filter(group == 3))$gene\ngene.group4 <- (temp %>% dplyr::filter(group == 4))$gene\ngene.group5 <- (temp %>% dplyr::filter(group == 5))$gene\ngene.group6 <- (temp %>% dplyr::filter(group == 6))$gene\ngene.group7 <- (temp %>% dplyr::filter(group == 7))$gene\ngene.group8 <- (temp %>% dplyr::filter(group == 8))$gene\ngene.group9 <- (temp %>% dplyr::filter(group == 9))$gene\n\nfwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 3)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group3.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 4)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group4.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 5)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 6)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group6.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 7)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group7.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 8)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 9)), here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group9.tsv\\), sep = \\\\t\\)\n\n\n# Adding group information to geneAnno\ngeneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
fcCutoff <- 0.5
diffCutoff <- 0.2
#
temp <- temp %>%
dplyr::mutate(group = ifelse(mean_diff_score < -diffCutoff,
ifelse(shrinked_log2FC < -fcCutoff, 1,
ifelse(shrinked_log2FC <fcCutoff, 2, 3)),
ifelse(mean_diff_score < diffCutoff,
ifelse(shrinked_log2FC < -fcCutoff, 4,
ifelse(shrinked_log2FC < fcCutoff, 5, 6)),
ifelse(shrinked_log2FC < -fcCutoff, 7,
ifelse(shrinked_log2FC < fcCutoff, 8, 9)))))
gene.group1 <- (temp %>% dplyr::filter(group == 1))$gene
gene.group2 <- (temp %>% dplyr::filter(group == 2))$gene
gene.group3 <- (temp %>% dplyr::filter(group == 3))$gene
gene.group4 <- (temp %>% dplyr::filter(group == 4))$gene
gene.group5 <- (temp %>% dplyr::filter(group == 5))$gene
gene.group6 <- (temp %>% dplyr::filter(group == 6))$gene
gene.group7 <- (temp %>% dplyr::filter(group == 7))$gene
gene.group8 <- (temp %>% dplyr::filter(group == 8))$gene
gene.group9 <- (temp %>% dplyr::filter(group == 9))$gene
fwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 3)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group3.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 4)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group4.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 5)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 6)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group6.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 7)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group7.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 8)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 9)), here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group9.tsv\), sep = \\t\)
# Adding group information to geneAnno
geneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Loop number per gene: P-S
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nGO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO5 <- enrichGO(gene = gene.group5, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO8 <- enrichGO(gene = gene.group8, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\n\nGO1.df <- as.data.frame(GO1)\nGO2.df <- as.data.frame(GO2)\nGO3.df <- as.data.frame(GO5)\nGO4.df <- as.data.frame(GO8)\n\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group1\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset2 <- GO2.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group2\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset3 <- GO3.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group3\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\nGOlist <- factor(c(\\GO:0033002\\, \\GO:0070373\\, \\GO:0048730\\, \\GO:0031103\\,\n                 \\GO:0022613\\, \\GO:0050767\\, \\GO:0034470\\, \\GO:0016055\\, \n                 \\GO:0006397\\, \\GO:0030900\\, \\GO:0008380\\))\n\ndata <- bind_rows(bind_rows(subset1, subset2), subset3) %>%\n  dplyr::filter(ID %in% GOlist)\n\ndescOrder <- sort(unique(data$Description))[c(1, 2, 5, 7,\n                                 3, 4, 6, 8, 9, 10, 11)]\n\np <- ggplot(data, aes(x = group, y = factor(Description, level = descOrder), color = p.adjust, size = gr)) + \n  geom_point() + theme_bw() +\n  scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n  scale_size_continuous(range = c(0, 3)) +\n  labs(x = NULL, y = NULL) +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups\\)\nheight = 2\nwidth = 3.5\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
GO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO5 <- enrichGO(gene = gene.group5, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO8 <- enrichGO(gene = gene.group8, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO1.df <- as.data.frame(GO1)
GO2.df <- as.data.frame(GO2)
GO3.df <- as.data.frame(GO5)
GO4.df <- as.data.frame(GO8)
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \group1\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset2 <- GO2.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \group2\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset3 <- GO3.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \group3\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
GOlist <- factor(c(\GO:0033002\, \GO:0070373\, \GO:0048730\, \GO:0031103\,
\GO:0022613\, \GO:0050767\, \GO:0034470\, \GO:0016055\,
\GO:0006397\, \GO:0030900\, \GO:0008380\))
data <- bind_rows(bind_rows(subset1, subset2), subset3) %>%
dplyr::filter(ID %in% GOlist)
descOrder <- sort(unique(data$Description))[c(1, 2, 5, 7,
3, 4, 6, 8, 9, 10, 11)]
p <- ggplot(data, aes(x = group, y = factor(Description, level = descOrder), color = p.adjust, size = gr)) +
geom_point() + theme_bw() +
scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
scale_size_continuous(range = c(0, 3)) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- here(figDir, \..\, \GO\, \GO_groups\)
height = 2
width = 3.5
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset2 <- GO2.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group2\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset3 <- GO3.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group3\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\nGOlist <- factor(c(\\GO:0033002\\, \\GO:0070373\\, \\GO:0048730\\, \\GO:0031103\\,\n                 \\GO:0022613\\, \\GO:0050767\\, \\GO:0034470\\, \\GO:0016055\\, \n                 \\GO:0006397\\, \\GO:0030900\\, \\GO:0008380\\))\n\ndata <- bind_rows(bind_rows(subset1, subset2), subset3) %>%\n  dplyr::filter(ID %in% GOlist)\n\ndescOrder <- sort(unique(data$Description))[c(1, 2, 5, 7,\n                                 3, 4, 6, 8, 9, 10, 11)]\n\np <- ggplot(data, aes(x = group, y = factor(Description, level = descOrder), color = p.adjust, size = gr)) + \n  geom_point() + theme_bw() +\n  scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n  scale_size_continuous(range = c(0, 3)) +\n  labs(x = NULL, y = NULL) +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups\\)\nheight = 2\nwidth = 3.5\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
GO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO5 <- enrichGO(gene = gene.group5, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO8 <- enrichGO(gene = gene.group8, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO1.df <- as.data.frame(GO1)
GO2.df <- as.data.frame(GO2)
GO3.df <- as.data.frame(GO5)
GO4.df <- as.data.frame(GO8)
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \group1\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset2 <- GO2.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \group2\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset3 <- GO3.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \group3\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
GOlist <- factor(c(\GO:0033002\, \GO:0070373\, \GO:0048730\, \GO:0031103\,
\GO:0022613\, \GO:0050767\, \GO:0034470\, \GO:0016055\,
\GO:0006397\, \GO:0030900\, \GO:0008380\))
data <- bind_rows(bind_rows(subset1, subset2), subset3) %>%
dplyr::filter(ID %in% GOlist)
descOrder <- sort(unique(data$Description))[c(1, 2, 5, 7,
3, 4, 6, 8, 9, 10, 11)]
p <- ggplot(data, aes(x = group, y = factor(Description, level = descOrder), color = p.adjust, size = gr)) +
geom_point() + theme_bw() +
scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
scale_size_continuous(range = c(0, 3)) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- here(figDir, \..\, \GO\, \GO_groups\)
height = 2
width = 3.5
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-text-begin -->
#### dTAG
##### - Splitting genes to groups
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# Checking average distance of loops per gene
# temp is a tibble where delta loop and log2fc are merged
temp$group <- factor(temp$group)
#
temp <- temp %>% dplyr::filter(group %in% c(1, 2, 5, 8))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$mean_distance
distance2 <- (data %>% dplyr::filter(group ==group2) )$mean_distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(temp, 1, 2), 5)
pv15 <- round(getPvalWilcox(temp, 1, 5), 5)
pv18 <- round(getPvalWilcox(temp, 1, 8), 5)
pv25 <- round(getPvalWilcox(temp, 2, 5), 5)
pv28 <- round(getPvalWilcox(temp, 2, 8), 5)
pv58 <- round(getPvalWilcox(temp, 5, 8), 5)
p <- ggplot(temp, aes(x = group, y = mean_distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + scale_y_continuous(labels = label_kb_mb) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 1000000, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\size_barplot_diffGroup_dTAG_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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```r
```r
# Checking average distance of loops per gene
# temp is a tibble where delta loop and log2fc are merged
temp$group <- factor(temp$group)
#
temp <- temp %>% dplyr::filter(group %in% c(1, 2, 5, 8))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$mean_distance
distance2 <- (data %>% dplyr::filter(group ==group2) )$mean_distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(temp, 1, 2), 5)
pv15 <- round(getPvalWilcox(temp, 1, 5), 5)
pv18 <- round(getPvalWilcox(temp, 1, 8), 5)
pv25 <- round(getPvalWilcox(temp, 2, 5), 5)
pv28 <- round(getPvalWilcox(temp, 2, 8), 5)
pv58 <- round(getPvalWilcox(temp, 5, 8), 5)
p <- ggplot(temp, aes(x = group, y = mean_distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + scale_y_continuous(labels = label_kb_mb) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 1000000, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\size_barplot_diffGroup_dTAG_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-text-begin -->
##### - GO for each group
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-->\n\n```r\n# Counting number of loop per genes\ntempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>%  summarize(\n    peak = list(peakID),\n    anno2 = list(Anno2),\n    count = n())\n\ntempSum <- tempSum %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n) %>%\n  dplyr::filter(group %in% c(\\group1\\, \\group2\\, \\group5\\, \\group8\\))\n\n\n# TEMP START\n#geneList.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))\n#geneList.group1.temp <- geneList.group1 %>% dplyr::left_join(tempSum, by = c(\\gene\\))\n# TEMP END\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\npv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\npv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\npv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\npv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\npv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\npv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(tempSum, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +       \n  scale_y_continuous(breaks = seq(0, 10, by = 2), limits = c(0, 10)) + \n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)  +\n  annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, pv12, \\\\n\\,\n                                                \\pv15: \\, pv15, \\\\n\\,\n                                                \\pv18: \\, pv18, \\\\n\\,\n                                                \\pv25: \\, pv25, \\\\n\\,\n                                                \\pv28: \\, pv28, \\\\n\\,\n                                                \\pv58: \\, pv58, \\\\n\\), \n           color = \\black\\, hjust = 0, size = 1) +  theme(legend.position = \\none\\)\n\n\nfileName <- paste0(\\count_barplot_diffGroup_dTAG_vs_DMSO\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# Counting number of loop per genes
tempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>% summarize(
peak = list(peakID),
anno2 = list(Anno2),
count = n())
tempSum <- tempSum %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
) %>%
dplyr::filter(group %in% c(\group1\, \group2\, \group5\, \group8\))
# TEMP START
#geneList.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))
#geneList.group1.temp <- geneList.group1 %>% dplyr::left_join(tempSum, by = c(\gene\))
# TEMP END
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$count
distance2 <- (data %>% dplyr::filter(group ==group2) )$count
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(tempSum,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(tempSum,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(tempSum,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(tempSum,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(tempSum,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(tempSum,\group5\, \group8\), 5)
p <- ggplot(tempSum, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2), limits = c(0, 10)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1) + theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n# Counting number of loop per genes\ntempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>%  summarize(\n    peak = list(peakID),\n    anno2 = list(Anno2),\n    count = n())\n\ntempSum <- tempSum %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n) %>%\n  dplyr::filter(group %in% c(\\group1\\, \\group2\\, \\group5\\, \\group8\\))\n\n\n# TEMP START\n#geneList.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))\n#geneList.group1.temp <- geneList.group1 %>% dplyr::left_join(tempSum, by = c(\\gene\\))\n# TEMP END\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\npv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\npv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\npv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\npv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\npv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\npv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(tempSum, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +       \n  scale_y_continuous(breaks = seq(0, 10, by = 2), limits = c(0, 10)) + \n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)  +\n  annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, pv12, \\\\n\\,\n                                                \\pv15: \\, pv15, \\\\n\\,\n                                                \\pv18: \\, pv18, \\\\n\\,\n                                                \\pv25: \\, pv25, \\\\n\\,\n                                                \\pv28: \\, pv28, \\\\n\\,\n                                                \\pv58: \\, pv58, \\\\n\\), \n           color = \\black\\, hjust = 0, size = 1) +  theme(legend.position = \\none\\)\n\n\nfileName <- paste0(\\count_barplot_diffGroup_dTAG_vs_DMSO\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
# Counting number of loop per genes
tempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>% summarize(
peak = list(peakID),
anno2 = list(Anno2),
count = n())
tempSum <- tempSum %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
) %>%
dplyr::filter(group %in% c(\group1\, \group2\, \group5\, \group8\))
# TEMP START
#geneList.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))
#geneList.group1.temp <- geneList.group1 %>% dplyr::left_join(tempSum, by = c(\gene\))
# TEMP END
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$count
distance2 <- (data %>% dplyr::filter(group ==group2) )$count
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(tempSum,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(tempSum,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(tempSum,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(tempSum,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(tempSum,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(tempSum,\group5\, \group8\), 5)
p <- ggplot(tempSum, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2), limits = c(0, 10)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1) + theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Average loop size
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
#######
temp2 <- tempSum %>% rowwise() %>% mutate(total = length(anno2),
num_pp = sum(anno2 == \P-P\),
num_pe = sum(anno2 == \P-E\),
num_ps = sum(anno2 == \P-S\),
num_px = sum(anno2 == \P-X\),
ratio_reg = (num_pp + num_pe)/total,
ratio_str = num_ps/total)
saveRDS(temp2, here(resultDir, \gene_loop_link.rds\))
loopType <- temp2 %>% group_by(group) %>% summarise(num_pp = sum(num_pp),
num_pe = sum(num_pe),
num_ps = sum(num_ps),
num_px = sum(num_px))
loopTypeLong <- loopType %>% pivot_longer(-group, names_to = \type\, values_to = \count\)
loopTypeLong$type <- factor(loopTypeLong$type, levels = c(\num_pp\, \num_pe\, \num_ps\, \num_px\))
# Plotting
ggplot(loopTypeLong, aes(fill=type, y=count, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
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```r
```r
#######
temp2 <- tempSum %>% rowwise() %>% mutate(total = length(anno2),
num_pp = sum(anno2 == \P-P\),
num_pe = sum(anno2 == \P-E\),
num_ps = sum(anno2 == \P-S\),
num_px = sum(anno2 == \P-X\),
ratio_reg = (num_pp + num_pe)/total,
ratio_str = num_ps/total)
saveRDS(temp2, here(resultDir, \gene_loop_link.rds\))
loopType <- temp2 %>% group_by(group) %>% summarise(num_pp = sum(num_pp),
num_pe = sum(num_pe),
num_ps = sum(num_ps),
num_px = sum(num_px))
loopTypeLong <- loopType %>% pivot_longer(-group, names_to = \type\, values_to = \count\)
loopTypeLong$type <- factor(loopTypeLong$type, levels = c(\num_pp\, \num_pe\, \num_ps\, \num_px\))
# Plotting
ggplot(loopTypeLong, aes(fill=type, y=count, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Loop number per gene: P-N
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
########
#P-P
data <- temp2 %>% dplyr::select(group, num_pp)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1)+ theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_pp\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
########
#P-P
data <- temp2 %>% dplyr::select(group, num_pp)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1)+ theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_pp\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### - Counting loop types
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<!-- rnb-output-begin 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-->
########
#P-E
data <- temp2 %>% dplyr::select(group, num_pe)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1)+ theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_pe\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
########
#P-E
data <- temp2 %>% dplyr::select(group, num_pe)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1)+ theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_pe\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### - Loop number per gene: P-P
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<!-- rnb-output-begin 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-->
########
#P-S
data <- temp2 %>% dplyr::select(group, num_ps)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1)+ theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_ps\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
########
#P-S
data <- temp2 %>% dplyr::select(group, num_ps)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1)+ theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_ps\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### - Loop number per gene: P-E
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<!-- rnb-output-begin 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-->
########
#P-X
data <- temp2 %>% dplyr::select(group, num_px)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1)+ theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_px\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
########
#P-X
data <- temp2 %>% dplyr::select(group, num_px)
colnames(data) <- c(\group\, \count\)
pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1)+ theme(legend.position = \none\)
fileName <- paste0(\count_barplot_diffGroup_dTAG_vs_DMSO_px\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### - Loop number per gene: P-S
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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  drop_na(shrinked_log2FC)\n\n\nfcCutoff <- 0.5\ndiffCutoff <- 0.2\n# \ntemp <- temp %>%\n  dplyr::mutate(group = ifelse(mean_diff_score < -diffCutoff,\n                               ifelse(shrinked_log2FC < -fcCutoff, 1,\n                                      ifelse(shrinked_log2FC <fcCutoff, 2, 3)),\n                               ifelse(mean_diff_score < diffCutoff,\n                                      ifelse(shrinked_log2FC < -fcCutoff, 4,\n                                             ifelse(shrinked_log2FC < fcCutoff, 5, 6)),\n                                      ifelse(shrinked_log2FC < -fcCutoff, 7,\n                                             ifelse(shrinked_log2FC < fcCutoff, 8, 9)))))\n\n\ngene.group1 <- (temp %>% dplyr::filter(group == 1))$gene\ngene.group2 <- (temp %>% dplyr::filter(group == 2))$gene\ngene.group3 <- (temp %>% dplyr::filter(group == 3))$gene\ngene.group4 <- (temp %>% dplyr::filter(group == 4))$gene\ngene.group5 <- (temp %>% dplyr::filter(group == 5))$gene\ngene.group6 <- (temp %>% dplyr::filter(group == 6))$gene\ngene.group7 <- (temp %>% dplyr::filter(group == 7))$gene\ngene.group8 <- (temp %>% dplyr::filter(group == 8))$gene\ngene.group9 <- (temp %>% dplyr::filter(group == 9))$gene\n\nfwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group1.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group2.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 3)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group3.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 4)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group4.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 5)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group5.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 6)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group6.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 7)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group7.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 8)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group8.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 9)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group9.tsv\\), sep = \\\\t\\)\n\n\n# Adding group information to geneAnno\ngeneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
fcCutoff <- 0.5
diffCutoff <- 0.2
#
temp <- temp %>%
dplyr::mutate(group = ifelse(mean_diff_score < -diffCutoff,
ifelse(shrinked_log2FC < -fcCutoff, 1,
ifelse(shrinked_log2FC <fcCutoff, 2, 3)),
ifelse(mean_diff_score < diffCutoff,
ifelse(shrinked_log2FC < -fcCutoff, 4,
ifelse(shrinked_log2FC < fcCutoff, 5, 6)),
ifelse(shrinked_log2FC < -fcCutoff, 7,
ifelse(shrinked_log2FC < fcCutoff, 8, 9)))))
gene.group1 <- (temp %>% dplyr::filter(group == 1))$gene
gene.group2 <- (temp %>% dplyr::filter(group == 2))$gene
gene.group3 <- (temp %>% dplyr::filter(group == 3))$gene
gene.group4 <- (temp %>% dplyr::filter(group == 4))$gene
gene.group5 <- (temp %>% dplyr::filter(group == 5))$gene
gene.group6 <- (temp %>% dplyr::filter(group == 6))$gene
gene.group7 <- (temp %>% dplyr::filter(group == 7))$gene
gene.group8 <- (temp %>% dplyr::filter(group == 8))$gene
gene.group9 <- (temp %>% dplyr::filter(group == 9))$gene
fwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group1.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group2.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 3)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group3.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 4)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group4.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 5)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group5.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 6)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group6.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 7)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group7.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 8)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group8.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 9)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group9.tsv\), sep = \\t\)
# Adding group information to geneAnno
geneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
)
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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_A485_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  drop_na(shrinked_log2FC)\n\n\nfcCutoff <- 0.5\ndiffCutoff <- 0.2\n# \ntemp <- temp %>%\n  dplyr::mutate(group = ifelse(mean_diff_score < -diffCutoff,\n                               ifelse(shrinked_log2FC < -fcCutoff, 1,\n                                      ifelse(shrinked_log2FC <fcCutoff, 2, 3)),\n                               ifelse(mean_diff_score < diffCutoff,\n                                      ifelse(shrinked_log2FC < -fcCutoff, 4,\n                                             ifelse(shrinked_log2FC < fcCutoff, 5, 6)),\n                                      ifelse(shrinked_log2FC < -fcCutoff, 7,\n                                             ifelse(shrinked_log2FC < fcCutoff, 8, 9)))))\n\n\ngene.group1 <- (temp %>% dplyr::filter(group == 1))$gene\ngene.group2 <- (temp %>% dplyr::filter(group == 2))$gene\ngene.group3 <- (temp %>% dplyr::filter(group == 3))$gene\ngene.group4 <- (temp %>% dplyr::filter(group == 4))$gene\ngene.group5 <- (temp %>% dplyr::filter(group == 5))$gene\ngene.group6 <- (temp %>% dplyr::filter(group == 6))$gene\ngene.group7 <- (temp %>% dplyr::filter(group == 7))$gene\ngene.group8 <- (temp %>% dplyr::filter(group == 8))$gene\ngene.group9 <- (temp %>% dplyr::filter(group == 9))$gene\n\nfwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group1.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group2.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 3)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group3.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 4)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group4.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 5)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group5.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 6)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group6.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 7)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group7.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 8)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group8.tsv\\), sep = \\\\t\\)\nfwrite((temp %>% dplyr::filter(group == 9)), here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group9.tsv\\), sep = \\\\t\\)\n\n\n# Adding group information to geneAnno\ngeneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
fcCutoff <- 0.5
diffCutoff <- 0.2
#
temp <- temp %>%
dplyr::mutate(group = ifelse(mean_diff_score < -diffCutoff,
ifelse(shrinked_log2FC < -fcCutoff, 1,
ifelse(shrinked_log2FC <fcCutoff, 2, 3)),
ifelse(mean_diff_score < diffCutoff,
ifelse(shrinked_log2FC < -fcCutoff, 4,
ifelse(shrinked_log2FC < fcCutoff, 5, 6)),
ifelse(shrinked_log2FC < -fcCutoff, 7,
ifelse(shrinked_log2FC < fcCutoff, 8, 9)))))
gene.group1 <- (temp %>% dplyr::filter(group == 1))$gene
gene.group2 <- (temp %>% dplyr::filter(group == 2))$gene
gene.group3 <- (temp %>% dplyr::filter(group == 3))$gene
gene.group4 <- (temp %>% dplyr::filter(group == 4))$gene
gene.group5 <- (temp %>% dplyr::filter(group == 5))$gene
gene.group6 <- (temp %>% dplyr::filter(group == 6))$gene
gene.group7 <- (temp %>% dplyr::filter(group == 7))$gene
gene.group8 <- (temp %>% dplyr::filter(group == 8))$gene
gene.group9 <- (temp %>% dplyr::filter(group == 9))$gene
fwrite((temp %>% dplyr::filter(group == 1)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group1.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 2)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group2.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 3)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group3.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 4)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group4.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 5)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group5.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 6)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group6.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 7)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group7.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 8)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group8.tsv\), sep = \\t\)
fwrite((temp %>% dplyr::filter(group == 9)), here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group9.tsv\), sep = \\t\)
# Adding group information to geneAnno
geneAnnoData <- geneAnnoData %>% unnest(gene) %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Loop number per gene: P-X
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nGO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO3 <- enrichGO(gene = gene.group3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO4 <- enrichGO(gene = gene.group4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO5 <- enrichGO(gene = gene.group5, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO6 <- enrichGO(gene = gene.group6, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO7 <- enrichGO(gene = gene.group7, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO8 <- enrichGO(gene = gene.group8, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO9 <- enrichGO(gene = gene.group9, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\n\ndotplot(GO1, showCategory = 10)\n\n# \n# GO1.df <- as.data.frame(GO1)\n# GO2.df <- as.data.frame(GO2)\n# GO3.df <- as.data.frame(GO5)\n# GO4.df <- as.data.frame(GO8)\n# \n# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group1\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset3 <- GO3.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group3\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# GOlist <- factor(c(\\GO:0033002\\, \\GO:0070373\\, \\GO:0048730\\, \\GO:0031103\\,\n#                  \\GO:0022613\\, \\GO:0050767\\, \\GO:0034470\\, \\GO:0016055\\, \n#                  \\GO:0006397\\, \\GO:0030900\\, \\GO:0008380\\))\n# \n# data <- bind_rows(bind_rows(subset1, subset2), subset3) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# descOrder <- sort(unique(data$Description))[c(1, 2, 5, 7,\n#                                  3, 4, 6, 8, 9, 10, 11)]\n# \n# p <- ggplot(data, aes(x = group, y = factor(Description, level = descOrder), color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups\\)\n# height = 2\n# width = 3.5\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
GO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO3 <- enrichGO(gene = gene.group3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO4 <- enrichGO(gene = gene.group4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO5 <- enrichGO(gene = gene.group5, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO6 <- enrichGO(gene = gene.group6, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO7 <- enrichGO(gene = gene.group7, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO8 <- enrichGO(gene = gene.group8, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO9 <- enrichGO(gene = gene.group9, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO1, showCategory = 10)
#
# GO1.df <- as.data.frame(GO1)
# GO2.df <- as.data.frame(GO2)
# GO3.df <- as.data.frame(GO5)
# GO4.df <- as.data.frame(GO8)
#
# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \group1\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \group2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset3 <- GO3.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \group3\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
# GOlist <- factor(c(\GO:0033002\, \GO:0070373\, \GO:0048730\, \GO:0031103\,
# \GO:0022613\, \GO:0050767\, \GO:0034470\, \GO:0016055\,
# \GO:0006397\, \GO:0030900\, \GO:0008380\))
#
# data <- bind_rows(bind_rows(subset1, subset2), subset3) %>%
# dplyr::filter(ID %in% GOlist)
#
# descOrder <- sort(unique(data$Description))[c(1, 2, 5, 7,
# 3, 4, 6, 8, 9, 10, 11)]
#
# p <- ggplot(data, aes(x = group, y = factor(Description, level = descOrder), color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups\)
# height = 2
# width = 3.5
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
<!-- rnb-output-end -->
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-->\n\n```r\n```r\nGO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO3 <- enrichGO(gene = gene.group3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO4 <- enrichGO(gene = gene.group4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO5 <- enrichGO(gene = gene.group5, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO6 <- enrichGO(gene = gene.group6, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO7 <- enrichGO(gene = gene.group7, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO8 <- enrichGO(gene = gene.group8, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO9 <- enrichGO(gene = gene.group9, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\n\ndotplot(GO1, showCategory = 10)\n\n# \n# GO1.df <- as.data.frame(GO1)\n# GO2.df <- as.data.frame(GO2)\n# GO3.df <- as.data.frame(GO5)\n# GO4.df <- as.data.frame(GO8)\n# \n# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group1\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset3 <- GO3.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \\group3\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# GOlist <- factor(c(\\GO:0033002\\, \\GO:0070373\\, \\GO:0048730\\, \\GO:0031103\\,\n#                  \\GO:0022613\\, \\GO:0050767\\, \\GO:0034470\\, \\GO:0016055\\, \n#                  \\GO:0006397\\, \\GO:0030900\\, \\GO:0008380\\))\n# \n# data <- bind_rows(bind_rows(subset1, subset2), subset3) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# descOrder <- sort(unique(data$Description))[c(1, 2, 5, 7,\n#                                  3, 4, 6, 8, 9, 10, 11)]\n# \n# p <- ggplot(data, aes(x = group, y = factor(Description, level = descOrder), color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups\\)\n# height = 2\n# width = 3.5\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
GO1 <- enrichGO(gene = gene.group1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO2 <- enrichGO(gene = gene.group2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO3 <- enrichGO(gene = gene.group3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO4 <- enrichGO(gene = gene.group4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO5 <- enrichGO(gene = gene.group5, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO6 <- enrichGO(gene = gene.group6, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO7 <- enrichGO(gene = gene.group7, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO8 <- enrichGO(gene = gene.group8, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO9 <- enrichGO(gene = gene.group9, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO1, showCategory = 10)
#
# GO1.df <- as.data.frame(GO1)
# GO2.df <- as.data.frame(GO2)
# GO3.df <- as.data.frame(GO5)
# GO4.df <- as.data.frame(GO8)
#
# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \group1\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \group2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset3 <- GO3.df %>% dplyr::select(ID, Description,GeneRatio, p.adjust) %>% dplyr::mutate(group = \group3\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
# GOlist <- factor(c(\GO:0033002\, \GO:0070373\, \GO:0048730\, \GO:0031103\,
# \GO:0022613\, \GO:0050767\, \GO:0034470\, \GO:0016055\,
# \GO:0006397\, \GO:0030900\, \GO:0008380\))
#
# data <- bind_rows(bind_rows(subset1, subset2), subset3) %>%
# dplyr::filter(ID %in% GOlist)
#
# descOrder <- sort(unique(data$Description))[c(1, 2, 5, 7,
# 3, 4, 6, 8, 9, 10, 11)]
#
# p <- ggplot(data, aes(x = group, y = factor(Description, level = descOrder), color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups\)
# height = 2
# width = 3.5
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
```
```
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#### A485
##### - Splitting genes to groups
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<!-- rnb-output-begin 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-->
# Checking average distance of loops per gene
# temp is a tibble where delta loop and log2fc are merged
temp$group <- factor(temp$group)
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group ==group1) )$mean_distance
# distance2 <- (data %>% dplyr::filter(group ==group2) )$mean_distance
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# pv12 <- round(getPvalWilcox(temp, 1, 2), 5)
# pv15 <- round(getPvalWilcox(temp, 1, 5), 5)
# pv18 <- round(getPvalWilcox(temp, 1, 8), 5)
# pv25 <- round(getPvalWilcox(temp, 2, 5), 5)
# pv28 <- round(getPvalWilcox(temp, 2, 8), 5)
# pv58 <- round(getPvalWilcox(temp, 5, 8), 5)
p <- ggplot(temp, aes(x = group, y = mean_distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + scale_y_continuous(labels = label_kb_mb) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
# annotate(\text\, x = 1, y = 1000000, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 3)
#
fileName <- paste0(\size_barplot_diffGroup_A485_vs_DMSO\)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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```r
```r
# Checking average distance of loops per gene
# temp is a tibble where delta loop and log2fc are merged
temp$group <- factor(temp$group)
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group ==group1) )$mean_distance
# distance2 <- (data %>% dplyr::filter(group ==group2) )$mean_distance
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# pv12 <- round(getPvalWilcox(temp, 1, 2), 5)
# pv15 <- round(getPvalWilcox(temp, 1, 5), 5)
# pv18 <- round(getPvalWilcox(temp, 1, 8), 5)
# pv25 <- round(getPvalWilcox(temp, 2, 5), 5)
# pv28 <- round(getPvalWilcox(temp, 2, 8), 5)
# pv58 <- round(getPvalWilcox(temp, 5, 8), 5)
p <- ggplot(temp, aes(x = group, y = mean_distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + scale_y_continuous(labels = label_kb_mb) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
# annotate(\text\, x = 1, y = 1000000, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 3)
#
fileName <- paste0(\size_barplot_diffGroup_A485_vs_DMSO\)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-text-begin -->
##### - GO for each group
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# Counting number of loop per genes\ntempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>%  summarize(\n    peak = list(peakID),\n    anno2 = list(Anno2),\n    count = n())\n\ntempSum <- tempSum %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n) %>% dplyr::filter(!is.na(group))\n\n# \n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n#   distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# \n# \n# pv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\n# pv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\n# pv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\n# pv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\n# pv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\n# pv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(tempSum, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +       \n  scale_y_continuous(breaks = seq(0, 10, by = 2)) + \n  coord_cartesian(ylim = c(0, 10)) +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme(legend.position = \\none\\)\n  # annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, pv12, \\\\n\\,\n  #                                               \\pv15: \\, pv15, \\\\n\\,\n  #                                               \\pv18: \\, pv18, \\\\n\\,\n  #                                               \\pv25: \\, pv25, \\\\n\\,\n  #                                               \\pv28: \\, pv28, \\\\n\\,\n  #                                               \\pv58: \\, pv58, \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 1) +  \n\n\nfileName <- paste0(\\count_barplot_diffGroup_A485_vs_DMSO\\)\nheight <- 3\nwidth <- 4\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# Counting number of loop per genes
tempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>% summarize(
peak = list(peakID),
anno2 = list(Anno2),
count = n())
tempSum <- tempSum %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
) %>% dplyr::filter(!is.na(group))
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group ==group1) )$count
# distance2 <- (data %>% dplyr::filter(group ==group2) )$count
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
#
# pv12 <- round(getPvalWilcox(tempSum,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(tempSum,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(tempSum,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(tempSum,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(tempSum,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(tempSum,\group5\, \group8\), 5)
p <- ggplot(tempSum, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
coord_cartesian(ylim = c(0, 10)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1) +
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO\)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# Counting number of loop per genes\ntempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>%  summarize(\n    peak = list(peakID),\n    anno2 = list(Anno2),\n    count = n())\n\ntempSum <- tempSum %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n) %>% dplyr::filter(!is.na(group))\n\n# \n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n#   distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# \n# \n# pv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\n# pv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\n# pv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\n# pv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\n# pv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\n# pv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(tempSum, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +       \n  scale_y_continuous(breaks = seq(0, 10, by = 2)) + \n  coord_cartesian(ylim = c(0, 10)) +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme(legend.position = \\none\\)\n  # annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, pv12, \\\\n\\,\n  #                                               \\pv15: \\, pv15, \\\\n\\,\n  #                                               \\pv18: \\, pv18, \\\\n\\,\n  #                                               \\pv25: \\, pv25, \\\\n\\,\n  #                                               \\pv28: \\, pv28, \\\\n\\,\n  #                                               \\pv58: \\, pv58, \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 1) +  \n\n\nfileName <- paste0(\\count_barplot_diffGroup_A485_vs_DMSO\\)\nheight <- 3\nwidth <- 4\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyBDb3VudGluZyBudW1iZXIgb2YgbG9vcCBwZXIgZ2VuZXNcbnRlbXBTdW0gPC0gZ2VuZUFubm9EYXRhICU+JSBkcGx5cjo6c2VsZWN0KHBlYWtJRCwgZ2VuZSwgQW5ubzIpICU+JSB1bm5lc3QoZ2VuZSkgJT4lIGdyb3VwX2J5KGdlbmUpICU+JSAgc3VtbWFyaXplKFxuICAgIHBlYWsgPSBsaXN0KHBlYWtJRCksXG4gICAgYW5ubzIgPSBsaXN0KEFubm8yKSxcbiAgICBjb3VudCA9IG4oKSlcblxudGVtcFN1bSA8LSB0ZW1wU3VtICU+JSBkcGx5cjo6bXV0YXRlKFxuICBncm91cCA9IGlmZWxzZShnZW5lICVpbiUgZ2VuZS5ncm91cDEsIFxcZ3JvdXAxXFwsXG4gICAgICAgICAgICAgICAgIGlmZWxzZShnZW5lICVpbiUgZ2VuZS5ncm91cDIsIFxcZ3JvdXAyXFwsXG4gICAgICAgICAgICAgICAgICAgICAgICBpZmVsc2UoZ2VuZSAlaW4lIGdlbmUuZ3JvdXAzLCBcXGdyb3VwM1xcLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGlmZWxzZShnZW5lICVpbiUgZ2VuZS5ncm91cDQsIFxcZ3JvdXA0XFwsXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGlmZWxzZShnZW5lICVpbiUgZ2VuZS5ncm91cDUsIFxcZ3JvdXA1XFwsXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBpZmVsc2UoZ2VuZSAlaW4lIGdlbmUuZ3JvdXA2LCBcXGdyb3VwNlxcLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGlmZWxzZShnZW5lICVpbiUgZ2VuZS5ncm91cDcsIFxcZ3JvdXA3XFwsXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGlmZWxzZShnZW5lICVpbiUgZ2VuZS5ncm91cDgsIFxcZ3JvdXA4XFwsXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBpZmVsc2UoZ2VuZSAlaW4lIGdlbmUuZ3JvdXA5LCBcXGdyb3VwOVxcLCBOQSkpKSkpKSkpKVxuKSAlPiUgZHBseXI6OmZpbHRlcighaXMubmEoZ3JvdXApKVxuXG4jIFxuIyBnZXRQdmFsV2lsY294IDwtIGZ1bmN0aW9uKGRhdGEsIGdyb3VwMSwgZ3JvdXAyKXtcbiMgICBkaXN0YW5jZTEgPC0gKGRhdGEgJT4lIGRwbHlyOjpmaWx0ZXIoZ3JvdXAgPT1ncm91cDEpICkkY291bnRcbiMgICBkaXN0YW5jZTIgPC0gKGRhdGEgJT4lIGRwbHlyOjpmaWx0ZXIoZ3JvdXAgPT1ncm91cDIpICkkY291bnRcbiMgICB3aWwgPC0gd2lsY294LnRlc3QoZGlzdGFuY2UxLCBkaXN0YW5jZTIpXG4jICAgcmV0dXJuKHdpbCRwLnZhbHVlKVxuIyB9XG4jIFxuIyBcbiMgcHYxMiA8LSByb3VuZChnZXRQdmFsV2lsY294KHRlbXBTdW0sXFxncm91cDFcXCwgXFxncm91cDJcXCksIDUpXG4jIHB2MTUgPC0gcm91bmQoZ2V0UHZhbFdpbGNveCh0ZW1wU3VtLFxcZ3JvdXAxXFwsIFxcZ3JvdXA1XFwpLCA1KVxuIyBwdjE4IDwtIHJvdW5kKGdldFB2YWxXaWxjb3godGVtcFN1bSxcXGdyb3VwMVxcLCBcXGdyb3VwOFxcKSwgNSlcbiMgcHYyNSA8LSByb3VuZChnZXRQdmFsV2lsY294KHRlbXBTdW0sXFxncm91cDJcXCwgXFxncm91cDVcXCksIDUpXG4jIHB2MjggPC0gcm91bmQoZ2V0UHZhbFdpbGNveCh0ZW1wU3VtLFxcZ3JvdXAyXFwsIFxcZ3JvdXA4XFwpLCA1KVxuIyBwdjU4IDwtIHJvdW5kKGdldFB2YWxXaWxjb3godGVtcFN1bSxcXGdyb3VwNVxcLCBcXGdyb3VwOFxcKSwgNSlcblxuXG5wIDwtIGdncGxvdCh0ZW1wU3VtLCBhZXMoeCA9IGdyb3VwLCB5ID0gY291bnQsIGZpbGwgPSBncm91cCkpICsgXG4gIGdlb21fYm94cGxvdCh3aWR0aCA9IDAuNSwgb3V0bGllci5zaGFwZSA9IE5BKSArIHRoZW1lX2NsYXNzaWMoKSArICAgICAgIFxuICBzY2FsZV95X2NvbnRpbnVvdXMoYnJlYWtzID0gc2VxKDAsIDEwLCBieSA9IDIpKSArIFxuICBjb29yZF9jYXJ0ZXNpYW4oeWxpbSA9IGMoMCwgMTApKSArXG4gIHN0YXRfc3VtbWFyeShhZXMoZ3JvdXAgPSBncm91cCksIGZ1biA9IG1lYW4sIGdlb20gPSBcXHBvaW50XFwsIHNoYXBlID0gMjEsIHNpemUgPSAyLCBmaWxsID0gXFxyZWRcXCwgY29sb3IgPSBcXGJsYWNrXFwpICtcbiAgdGhlbWUobGVnZW5kLnBvc2l0aW9uID0gXFxub25lXFwpXG4gICMgYW5ub3RhdGUoXFx0ZXh0XFwsIHggPSAxLCB5ID0gMywgbGFiZWwgPSBwYXN0ZTAoXFxwdjEyOiBcXCwgcHYxMiwgXFxcXG5cXCxcbiAgIyAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgXFxwdjE1OiBcXCwgcHYxNSwgXFxcXG5cXCxcbiAgIyAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgXFxwdjE4OiBcXCwgcHYxOCwgXFxcXG5cXCxcbiAgIyAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgXFxwdjI1OiBcXCwgcHYyNSwgXFxcXG5cXCxcbiAgIyAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgXFxwdjI4OiBcXCwgcHYyOCwgXFxcXG5cXCxcbiAgIyAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgXFxwdjU4OiBcXCwgcHY1OCwgXFxcXG5cXCksIFxuICAjICAgICAgICAgIGNvbG9yID0gXFxibGFja1xcLCBoanVzdCA9IDAsIHNpemUgPSAxKSArICBcblxuXG5maWxlTmFtZSA8LSBwYXN0ZTAoXFxjb3VudF9iYXJwbG90X2RpZmZHcm91cF9BNDg1X3ZzX0RNU09cXClcbmhlaWdodCA8LSAzXG53aWR0aCA8LSA0XG5wbmcoaGVyZShmaWdEaXIsIHBhc3RlMChmaWxlTmFtZSwgXFwucG5nXFwpKSwgcmVzID0gNjAwLCB1bml0ID0gXFxpblxcLCBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG5wcmludChwKVxuZGV2Lm9mZigpXG5zdmdsaXRlKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnN2Z1xcKSksICBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG5wcmludChwKVxuZGV2Lm9mZigpXG5gYGBcbmBgYCJ9 -->
```r
```r
# Counting number of loop per genes
tempSum <- geneAnnoData %>% dplyr::select(peakID, gene, Anno2) %>% unnest(gene) %>% group_by(gene) %>% summarize(
peak = list(peakID),
anno2 = list(Anno2),
count = n())
tempSum <- tempSum %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
) %>% dplyr::filter(!is.na(group))
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group ==group1) )$count
# distance2 <- (data %>% dplyr::filter(group ==group2) )$count
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
#
# pv12 <- round(getPvalWilcox(tempSum,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(tempSum,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(tempSum,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(tempSum,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(tempSum,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(tempSum,\group5\, \group8\), 5)
p <- ggplot(tempSum, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 10, by = 2)) +
coord_cartesian(ylim = c(0, 10)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1) +
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO\)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Average loop size
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
#######
temp2 <- tempSum %>% rowwise() %>% mutate(total = length(anno2),
num_pp = sum(anno2 == \P-P\),
num_pe = sum(anno2 == \P-E\),
num_ps = sum(anno2 == \P-S\),
num_px = sum(anno2 == \P-X\),
ratio_reg = (num_pp + num_pe)/total,
ratio_str = num_ps/total)
saveRDS(temp2, here(resultDir, \gene_loop_link_A485.rds\))
loopType <- temp2 %>% group_by(group) %>% summarise(num_pp = sum(num_pp),
num_pe = sum(num_pe),
num_ps = sum(num_ps),
num_px = sum(num_px))
loopTypeLong <- loopType %>% pivot_longer(-group, names_to = \type\, values_to = \count\)
loopTypeLong$type <- factor(loopTypeLong$type, levels = c(\num_pp\, \num_pe\, \num_ps\, \num_px\))
# Plotting
ggplot(loopTypeLong, aes(fill=type, y=count, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
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```r
```r
#######
temp2 <- tempSum %>% rowwise() %>% mutate(total = length(anno2),
num_pp = sum(anno2 == \P-P\),
num_pe = sum(anno2 == \P-E\),
num_ps = sum(anno2 == \P-S\),
num_px = sum(anno2 == \P-X\),
ratio_reg = (num_pp + num_pe)/total,
ratio_str = num_ps/total)
saveRDS(temp2, here(resultDir, \gene_loop_link_A485.rds\))
loopType <- temp2 %>% group_by(group) %>% summarise(num_pp = sum(num_pp),
num_pe = sum(num_pe),
num_ps = sum(num_ps),
num_px = sum(num_px))
loopTypeLong <- loopType %>% pivot_longer(-group, names_to = \type\, values_to = \count\)
loopTypeLong$type <- factor(loopTypeLong$type, levels = c(\num_pp\, \num_pe\, \num_ps\, \num_px\))
# Plotting
ggplot(loopTypeLong, aes(fill=type, y=count, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Loop number per gene: P-N
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
########
#P-P
data <- temp2 %>% dplyr::select(group, num_pp)
colnames(data) <- c(\group\, \count\)
# pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1)
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO_pp\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyMjIyMjIyNcbiNQLVBcbmRhdGEgPC0gdGVtcDIgJT4lIGRwbHlyOjpzZWxlY3QoZ3JvdXAsIG51bV9wcClcbmNvbG5hbWVzKGRhdGEpIDwtIGMoXFxncm91cFxcLCBcXGNvdW50XFwpXG4jIHB2MTIgPC0gcm91bmQoZ2V0UHZhbFdpbGNveChkYXRhLFxcZ3JvdXAxXFwsIFxcZ3JvdXAyXFwpLCA1KVxuIyBwdjE1IDwtIHJvdW5kKGdldFB2YWxXaWxjb3goZGF0YSxcXGdyb3VwMVxcLCBcXGdyb3VwNVxcKSwgNSlcbiMgcHYxOCA8LSByb3VuZChnZXRQdmFsV2lsY294KGRhdGEsXFxncm91cDFcXCwgXFxncm91cDhcXCksIDUpXG4jIHB2MjUgPC0gcm91bmQoZ2V0UHZhbFdpbGNveChkYXRhLFxcZ3JvdXAyXFwsIFxcZ3JvdXA1XFwpLCA1KVxuIyBwdjI4IDwtIHJvdW5kKGdldFB2YWxXaWxjb3goZGF0YSxcXGdyb3VwMlxcLCBcXGdyb3VwOFxcKSwgNSlcbiMgcHY1OCA8LSByb3VuZChnZXRQdmFsV2lsY294KGRhdGEsXFxncm91cDVcXCwgXFxncm91cDhcXCksIDUpXG5cbnAgPC0gZ2dwbG90KGRhdGEsIGFlcyh4ID0gZ3JvdXAsIHkgPSBjb3VudCwgZmlsbCA9IGdyb3VwKSkgK1xuICBnZW9tX2JveHBsb3Qod2lkdGggPSAwLjUsIG91dGxpZXIuc2hhcGUgPSBOQSkgKyB0aGVtZV9jbGFzc2ljKCkgKyAgICAgICBcbiAgc2NhbGVfeV9jb250aW51b3VzKGJyZWFrcyA9IHNlcSgwLCA1LCBieSA9IDIpLCBsaW1pdHMgPSBjKDAsIDUpKSArIFxuICBzdGF0X3N1bW1hcnkoYWVzKGdyb3VwID0gZ3JvdXApLCBmdW4gPSBtZWFuLCBnZW9tID0gXFxwb2ludFxcLCBzaGFwZSA9IDIxLCBzaXplID0gMiwgZmlsbCA9IFxccmVkXFwsIGNvbG9yID0gXFxibGFja1xcKSAgK1xuICB0aGVtZShsZWdlbmQucG9zaXRpb24gPSBcXG5vbmVcXClcbiAgIyBhbm5vdGF0ZShcXHRleHRcXCwgeCA9IDEsIHkgPSAzLCBsYWJlbCA9IHBhc3RlMChcXHB2MTI6IFxcLCBwdjEyLCBcXFxcblxcLFxuICAjICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBcXHB2MTU6IFxcLCBwdjE1LCBcXFxcblxcLFxuICAjICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBcXHB2MTg6IFxcLCBwdjE4LCBcXFxcblxcLFxuICAjICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBcXHB2MjU6IFxcLCBwdjI1LCBcXFxcblxcLFxuICAjICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBcXHB2Mjg6IFxcLCBwdjI4LCBcXFxcblxcLFxuICAjICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBcXHB2NTg6IFxcLCBwdjU4LCBcXFxcblxcKSwgXG4gICMgICAgICAgICAgY29sb3IgPSBcXGJsYWNrXFwsIGhqdXN0ID0gMCwgc2l6ZSA9IDEpXG5cblxuZmlsZU5hbWUgPC0gcGFzdGUwKFxcY291bnRfYmFycGxvdF9kaWZmR3JvdXBfQTQ4NV92c19ETVNPX3BwXFwpXG5oZWlnaHQgPC0gM1xud2lkdGggPC0gM1xucG5nKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnBuZ1xcKSksIHJlcyA9IDYwMCwgdW5pdCA9IFxcaW5cXCwgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuc3ZnbGl0ZShoZXJlKGZpZ0RpciwgcGFzdGUwKGZpbGVOYW1lLCBcXC5zdmdcXCkpLCAgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuYGBgXG5gYGAifQ== -->
```r
```r
########
#P-P
data <- temp2 %>% dplyr::select(group, num_pp)
colnames(data) <- c(\group\, \count\)
# pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1)
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO_pp\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Counting loop types
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
########
#P-E
data <- temp2 %>% dplyr::select(group, num_pe)
colnames(data) <- c(\group\, \count\)
# pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1)
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO_pe\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
########
#P-E
data <- temp2 %>% dplyr::select(group, num_pe)
colnames(data) <- c(\group\, \count\)
# pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1)
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO_pe\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### - Loop number per gene: P-P
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<!-- rnb-output-begin 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-->
########
#P-S
data <- temp2 %>% dplyr::select(group, num_ps)
colnames(data) <- c(\group\, \count\)
# pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1)+
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO_ps\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
########
#P-S
data <- temp2 %>% dplyr::select(group, num_ps)
colnames(data) <- c(\group\, \count\)
# pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1)+
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO_ps\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### - Loop number per gene: P-E
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<!-- rnb-output-begin 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-->
########
#P-X
data <- temp2 %>% dplyr::select(group, num_px)
colnames(data) <- c(\group\, \count\)
# pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1)+
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO_px\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
########
#P-X
data <- temp2 %>% dplyr::select(group, num_px)
colnames(data) <- c(\group\, \count\)
# pv12 <- round(getPvalWilcox(data,\group1\, \group2\), 5)
# pv15 <- round(getPvalWilcox(data,\group1\, \group5\), 5)
# pv18 <- round(getPvalWilcox(data,\group1\, \group8\), 5)
# pv25 <- round(getPvalWilcox(data,\group2\, \group5\), 5)
# pv28 <- round(getPvalWilcox(data,\group2\, \group8\), 5)
# pv58 <- round(getPvalWilcox(data,\group5\, \group8\), 5)
p <- ggplot(data, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 5, by = 2), limits = c(0, 5)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme(legend.position = \none\)
# annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
# \pv15: \, pv15, \\n\,
# \pv18: \, pv18, \\n\,
# \pv25: \, pv25, \\n\,
# \pv28: \, pv28, \\n\,
# \pv58: \, pv58, \\n\),
# color = \black\, hjust = 0, size = 1)+
fileName <- paste0(\count_barplot_diffGroup_A485_vs_DMSO_px\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### - Loop number per gene: P-S
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start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n\n# temp2 contains genes from group 1, 2, 5, 8 and loop counts\n#calculating diff score and log2fc distribution based on p-n numbers\nresultDir <- here(\\../../result\\)\n\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\n\npnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene\npnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene\npnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene\npnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene\npnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene\n\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nmaxLog2FC = 2\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \\2DOWN\\, \\0NO\\),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag) %>%\n  drop_na(shrinked_log2FC)\n\ntemp <- temp %>% dplyr::mutate(\n  pnOver = ifelse(gene %in% pnOver8, \\pnOver8\\,\n                  ifelse(gene %in% pnOver6, \\pnOver6\\,\n                         ifelse(gene %in% pnOver4, \\pnOver4\\,\n                                ifelse(gene %in% pnOver2, \\pnOver2\\,\n                                       ifelse(gene %in% pnOver0, \\pnOver0\\, NA)))))) %>%\n  drop_na(pnOver)\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
############
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
pnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene
pnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene
pnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene
pnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene
pnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
pnOver = ifelse(gene %in% pnOver8, \pnOver8\,
ifelse(gene %in% pnOver6, \pnOver6\,
ifelse(gene %in% pnOver4, \pnOver4\,
ifelse(gene %in% pnOver2, \pnOver2\,
ifelse(gene %in% pnOver0, \pnOver0\, NA)))))) %>%
drop_na(pnOver)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.PRO.G1.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.PRO.G2.dTAG <- fread(here(refDir, \\diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\n############\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n\n# temp2 contains genes from group 1, 2, 5, 8 and loop counts\n#calculating diff score and log2fc distribution based on p-n numbers\nresultDir <- here(\\../../result\\)\n\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\n\npnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene\npnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene\npnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene\npnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene\npnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene\n\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nmaxLog2FC = 2\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \\2DOWN\\, \\0NO\\),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag) %>%\n  drop_na(shrinked_log2FC)\n\ntemp <- temp %>% dplyr::mutate(\n  pnOver = ifelse(gene %in% pnOver8, \\pnOver8\\,\n                  ifelse(gene %in% pnOver6, \\pnOver6\\,\n                         ifelse(gene %in% pnOver4, \\pnOver4\\,\n                                ifelse(gene %in% pnOver2, \\pnOver2\\,\n                                       ifelse(gene %in% pnOver0, \\pnOver0\\, NA)))))) %>%\n  drop_na(pnOver)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
name <- \chromo_cons_annoHierarchy\
alpha <- 0.05
fcCutoff <- 0.5
diff.PRO.G1.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G1.dTAG_vs_G1.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.PRO.G2.dTAG <- fread(here(refDir, \diff_G1G2.dTAG_G2.dTAG_vs_G2.DMSO_PROseq.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
############
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
pnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene
pnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene
pnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene
pnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene
pnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
pnOver = ifelse(gene %in% pnOver8, \pnOver8\,
ifelse(gene %in% pnOver6, \pnOver6\,
ifelse(gene %in% pnOver4, \pnOver4\,
ifelse(gene %in% pnOver2, \pnOver2\,
ifelse(gene %in% pnOver0, \pnOver0\, NA)))))) %>%
drop_na(pnOver)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Loop number per gene: P-X
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nGOfigDir <- here(figDir, \\../GO\\)\ngetGO(\\pnOver8\\, GOfigDir, pnOver8)\ngetGO(\\pnOver6\\, GOfigDir, pnOver6)\ngetGO(\\pnOver4\\, GOfigDir, pnOver4)\ngetGO(\\pnOver2\\, GOfigDir, pnOver2)\ngetGO(\\pnOver0\\, GOfigDir, pnOver0)\n\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = pnOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO2.df <- as.data.frame(enrichGO(gene = pnOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO4.df <- as.data.frame(enrichGO(gene = pnOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO6.df <- as.data.frame(enrichGO(gene = pnOver6, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO8.df <- as.data.frame(enrichGO(gene = pnOver8, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver0\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver2\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver4\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset6 <- GO6.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver6\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset8 <- GO8.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver8\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\n\nGOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n                   \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n                   \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\))\n\ndata <- bind_rows(bind_rows(bind_rows(subset0, subset2), subset4), subset8) %>%\n  dplyr::filter(ID %in% GOlist)\n\np <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n  geom_point() + theme_bw() +\n  scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n  scale_size_continuous(range = c(0, 3)) +\n  labs(x = NULL, y = NULL) +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pn\\)\nheight = 2\nwidth = 3.3\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
GOfigDir <- here(figDir, \../GO\)
getGO(\pnOver8\, GOfigDir, pnOver8)
getGO(\pnOver6\, GOfigDir, pnOver6)
getGO(\pnOver4\, GOfigDir, pnOver4)
getGO(\pnOver2\, GOfigDir, pnOver2)
getGO(\pnOver0\, GOfigDir, pnOver0)
#####################
GO0.df <- as.data.frame(enrichGO(gene = pnOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO2.df <- as.data.frame(enrichGO(gene = pnOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO4.df <- as.data.frame(enrichGO(gene = pnOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO6.df <- as.data.frame(enrichGO(gene = pnOver6, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO8.df <- as.data.frame(enrichGO(gene = pnOver8, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver0\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver2\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver4\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset6 <- GO6.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver6\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset8 <- GO8.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver8\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
\GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
\GO:0072001\, \GO:0007517\, \GO:0048705\))
data <- bind_rows(bind_rows(bind_rows(subset0, subset2), subset4), subset8) %>%
dplyr::filter(ID %in% GOlist)
p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
geom_point() + theme_bw() +
scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
scale_size_continuous(range = c(0, 3)) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- here(figDir, \..\, \GO\, \GO_groups_pn\)
height = 2
width = 3.3
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\nGOfigDir <- here(figDir, \\../GO\\)\ngetGO(\\pnOver8\\, GOfigDir, pnOver8)\ngetGO(\\pnOver6\\, GOfigDir, pnOver6)\ngetGO(\\pnOver4\\, GOfigDir, pnOver4)\ngetGO(\\pnOver2\\, GOfigDir, pnOver2)\ngetGO(\\pnOver0\\, GOfigDir, pnOver0)\n\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = pnOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO2.df <- as.data.frame(enrichGO(gene = pnOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO4.df <- as.data.frame(enrichGO(gene = pnOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO6.df <- as.data.frame(enrichGO(gene = pnOver6, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO8.df <- as.data.frame(enrichGO(gene = pnOver8, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver0\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver2\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver4\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset6 <- GO6.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver6\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\nsubset8 <- GO8.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver8\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\n\nGOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n                   \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n                   \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\))\n\ndata <- bind_rows(bind_rows(bind_rows(subset0, subset2), subset4), subset8) %>%\n  dplyr::filter(ID %in% GOlist)\n\np <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n  geom_point() + theme_bw() +\n  scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n  scale_size_continuous(range = c(0, 3)) +\n  labs(x = NULL, y = NULL) +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pn\\)\nheight = 2\nwidth = 3.3\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
GOfigDir <- here(figDir, \../GO\)
getGO(\pnOver8\, GOfigDir, pnOver8)
getGO(\pnOver6\, GOfigDir, pnOver6)
getGO(\pnOver4\, GOfigDir, pnOver4)
getGO(\pnOver2\, GOfigDir, pnOver2)
getGO(\pnOver0\, GOfigDir, pnOver0)
#####################
GO0.df <- as.data.frame(enrichGO(gene = pnOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO2.df <- as.data.frame(enrichGO(gene = pnOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO4.df <- as.data.frame(enrichGO(gene = pnOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO6.df <- as.data.frame(enrichGO(gene = pnOver6, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO8.df <- as.data.frame(enrichGO(gene = pnOver8, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver0\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver2\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver4\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset6 <- GO6.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver6\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset8 <- GO8.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver8\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
\GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
\GO:0072001\, \GO:0007517\, \GO:0048705\))
data <- bind_rows(bind_rows(bind_rows(subset0, subset2), subset4), subset8) %>%
dplyr::filter(ID %in% GOlist)
p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
geom_point() + theme_bw() +
scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
scale_size_continuous(range = c(0, 3)) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- here(figDir, \..\, \GO\, \GO_groups_pn\)
height = 2
width = 3.3
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### dTAG
#### Grouping with P-N number
##### Grouping
<!-- rnb-text-end -->
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-->
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv02 <- round(getPvalWilcox(temp, \pnOver0\, \pnOver2\), 5)
pv24 <- round(getPvalWilcox(temp, \pnOver2\, \pnOver4\), 5)
pv46 <- round(getPvalWilcox(temp, \pnOver4\, \pnOver6\), 5)
pv08 <- round(getPvalWilcox(temp, \pnOver0\, \pnOver8\), 5)
pv28 <- round(getPvalWilcox(temp, \pnOver2\, \pnOver8\), 5)
pv48 <- round(getPvalWilcox(temp, \pnOver4\, \pnOver8\), 5)
pv68 <- round(getPvalWilcox(temp, \pnOver6\, \pnOver8\), 5)
p <- ggplot(temp, aes(x = pnOver, y = mean_diff_score)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0.2)+
annotate(\text\, x = 1, y = 0, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
\pv24: \, convPvalue(pv24), \\n\,
\pv46: \, convPvalue(pv46), \\n\,
\pv68: \, convPvalue(pv68), \\n\,
\pv48: \, convPvalue(pv48), \\n\,
\pv28: \, convPvalue(pv28), \\n\,
\pv08: \, convPvalue(pv08), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_pnGroup_dTAG_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->
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```r
```r
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv02 <- round(getPvalWilcox(temp, \pnOver0\, \pnOver2\), 5)
pv24 <- round(getPvalWilcox(temp, \pnOver2\, \pnOver4\), 5)
pv46 <- round(getPvalWilcox(temp, \pnOver4\, \pnOver6\), 5)
pv08 <- round(getPvalWilcox(temp, \pnOver0\, \pnOver8\), 5)
pv28 <- round(getPvalWilcox(temp, \pnOver2\, \pnOver8\), 5)
pv48 <- round(getPvalWilcox(temp, \pnOver4\, \pnOver8\), 5)
pv68 <- round(getPvalWilcox(temp, \pnOver6\, \pnOver8\), 5)
p <- ggplot(temp, aes(x = pnOver, y = mean_diff_score)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0.2)+
annotate(\text\, x = 1, y = 0, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
\pv24: \, convPvalue(pv24), \\n\,
\pv46: \, convPvalue(pv46), \\n\,
\pv68: \, convPvalue(pv68), \\n\,
\pv48: \, convPvalue(pv48), \\n\,
\pv28: \, convPvalue(pv28), \\n\,
\pv08: \, convPvalue(pv08), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_pnGroup_dTAG_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### GO for each group
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \npv02 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver2\\), 5)\npv04 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver4\\), 5)\npv06 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver6\\), 5)\npv08 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver8\\), 5)\npv24 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver4\\), 5)\npv26 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver6\\), 5)\npv28 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver8\\), 5)\npv46 <- round(getPvalWilcox(tempDown, \\pnOver4\\, \\pnOver6\\), 5)\npv48 <- round(getPvalWilcox(tempDown, \\pnOver4\\, \\pnOver8\\), 5)\npv68 <- round(getPvalWilcox(tempDown, \\pnOver6\\, \\pnOver8\\), 5)\n\n\n\n\np <- ggplot(tempDown, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = pnOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\pv02: \\, convPvalue(pv02), \\\\n\\,\n                                                   \\pv04: \\, convPvalue(pv04), \\\\n\\,\n                                                   \\pv06: \\, convPvalue(pv06), \\\\n\\,\n                                                   \\pv08: \\, convPvalue(pv08), \\\\n\\,\n                                                   \\pv24: \\, convPvalue(pv24), \\\\n\\,\n                                                   \\pv26: \\, convPvalue(pv26), \\\\n\\,\n                                                   \\pv28: \\, convPvalue(pv28), \\\\n\\,\n                                                   \\pv46: \\, convPvalue(pv46), \\\\n\\,\n                                                   \\pv48: \\, convPvalue(pv48), \\\\n\\,\n                                                   \\pv68: \\, convPvalue(pv68), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_pnGroup_dTAG_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \npv02 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver2\\), 5)\npv04 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver4\\), 5)\npv06 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver6\\), 5)\npv08 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver8\\), 5)\npv24 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver4\\), 5)\npv26 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver6\\), 5)\npv28 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver8\\), 5)\npv46 <- round(getPvalWilcox(tempUp, \\pnOver4\\, \\pnOver6\\), 5)\npv48 <- round(getPvalWilcox(tempUp, \\pnOver4\\, \\pnOver8\\), 5)\npv68 <- round(getPvalWilcox(tempUp, \\pnOver6\\, \\pnOver8\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = pnOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\pv02: \\, convPvalue(pv02), \\\\n\\,\n                                                   \\pv04: \\, convPvalue(pv04), \\\\n\\,\n                                                   \\pv06: \\, convPvalue(pv06), \\\\n\\,\n                                                   \\pv08: \\, convPvalue(pv08), \\\\n\\,\n                                                   \\pv24: \\, convPvalue(pv24), \\\\n\\,\n                                                   \\pv26: \\, convPvalue(pv26), \\\\n\\,\n                                                   \\pv28: \\, convPvalue(pv28), \\\\n\\,\n                                                   \\pv46: \\, convPvalue(pv46), \\\\n\\,\n                                                   \\pv48: \\, convPvalue(pv48), \\\\n\\,\n                                                   \\pv68: \\, convPvalue(pv68), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_pnGroup_dTAG_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$log2FoldChange
distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$log2FoldChange
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
pv02 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver2\), 5)
pv04 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver4\), 5)
pv06 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver6\), 5)
pv08 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver8\), 5)
pv24 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver4\), 5)
pv26 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver6\), 5)
pv28 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver8\), 5)
pv46 <- round(getPvalWilcox(tempDown, \pnOver4\, \pnOver6\), 5)
pv48 <- round(getPvalWilcox(tempDown, \pnOver4\, \pnOver8\), 5)
pv68 <- round(getPvalWilcox(tempDown, \pnOver6\, \pnOver8\), 5)
p <- ggplot(tempDown, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)+
annotate(\text\, x = 1, y = -0.5, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
\pv04: \, convPvalue(pv04), \\n\,
\pv06: \, convPvalue(pv06), \\n\,
\pv08: \, convPvalue(pv08), \\n\,
\pv24: \, convPvalue(pv24), \\n\,
\pv26: \, convPvalue(pv26), \\n\,
\pv28: \, convPvalue(pv28), \\n\,
\pv46: \, convPvalue(pv46), \\n\,
\pv48: \, convPvalue(pv48), \\n\,
\pv68: \, convPvalue(pv68), \\n\),
color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_pnGroup_dTAG_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
pv02 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver2\), 5)
pv04 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver4\), 5)
pv06 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver6\), 5)
pv08 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver8\), 5)
pv24 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver4\), 5)
pv26 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver6\), 5)
pv28 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver8\), 5)
pv46 <- round(getPvalWilcox(tempUp, \pnOver4\, \pnOver6\), 5)
pv48 <- round(getPvalWilcox(tempUp, \pnOver4\, \pnOver8\), 5)
pv68 <- round(getPvalWilcox(tempUp, \pnOver6\, \pnOver8\), 5)
p <- ggplot(tempUp, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)+
annotate(\text\, x = 1, y = 0.5, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
\pv04: \, convPvalue(pv04), \\n\,
\pv06: \, convPvalue(pv06), \\n\,
\pv08: \, convPvalue(pv08), \\n\,
\pv24: \, convPvalue(pv24), \\n\,
\pv26: \, convPvalue(pv26), \\n\,
\pv28: \, convPvalue(pv28), \\n\,
\pv46: \, convPvalue(pv46), \\n\,
\pv48: \, convPvalue(pv48), \\n\,
\pv68: \, convPvalue(pv68), \\n\),
color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_pnGroup_dTAG_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \npv02 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver2\\), 5)\npv04 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver4\\), 5)\npv06 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver6\\), 5)\npv08 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver8\\), 5)\npv24 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver4\\), 5)\npv26 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver6\\), 5)\npv28 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver8\\), 5)\npv46 <- round(getPvalWilcox(tempDown, \\pnOver4\\, \\pnOver6\\), 5)\npv48 <- round(getPvalWilcox(tempDown, \\pnOver4\\, \\pnOver8\\), 5)\npv68 <- round(getPvalWilcox(tempDown, \\pnOver6\\, \\pnOver8\\), 5)\n\n\n\n\np <- ggplot(tempDown, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = pnOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\pv02: \\, convPvalue(pv02), \\\\n\\,\n                                                   \\pv04: \\, convPvalue(pv04), \\\\n\\,\n                                                   \\pv06: \\, convPvalue(pv06), \\\\n\\,\n                                                   \\pv08: \\, convPvalue(pv08), \\\\n\\,\n                                                   \\pv24: \\, convPvalue(pv24), \\\\n\\,\n                                                   \\pv26: \\, convPvalue(pv26), \\\\n\\,\n                                                   \\pv28: \\, convPvalue(pv28), \\\\n\\,\n                                                   \\pv46: \\, convPvalue(pv46), \\\\n\\,\n                                                   \\pv48: \\, convPvalue(pv48), \\\\n\\,\n                                                   \\pv68: \\, convPvalue(pv68), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_pnGroup_dTAG_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \npv02 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver2\\), 5)\npv04 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver4\\), 5)\npv06 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver6\\), 5)\npv08 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver8\\), 5)\npv24 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver4\\), 5)\npv26 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver6\\), 5)\npv28 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver8\\), 5)\npv46 <- round(getPvalWilcox(tempUp, \\pnOver4\\, \\pnOver6\\), 5)\npv48 <- round(getPvalWilcox(tempUp, \\pnOver4\\, \\pnOver8\\), 5)\npv68 <- round(getPvalWilcox(tempUp, \\pnOver6\\, \\pnOver8\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = pnOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\pv02: \\, convPvalue(pv02), \\\\n\\,\n                                                   \\pv04: \\, convPvalue(pv04), \\\\n\\,\n                                                   \\pv06: \\, convPvalue(pv06), \\\\n\\,\n                                                   \\pv08: \\, convPvalue(pv08), \\\\n\\,\n                                                   \\pv24: \\, convPvalue(pv24), \\\\n\\,\n                                                   \\pv26: \\, convPvalue(pv26), \\\\n\\,\n                                                   \\pv28: \\, convPvalue(pv28), \\\\n\\,\n                                                   \\pv46: \\, convPvalue(pv46), \\\\n\\,\n                                                   \\pv48: \\, convPvalue(pv48), \\\\n\\,\n                                                   \\pv68: \\, convPvalue(pv68), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_pnGroup_dTAG_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$log2FoldChange
distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$log2FoldChange
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
pv02 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver2\), 5)
pv04 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver4\), 5)
pv06 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver6\), 5)
pv08 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver8\), 5)
pv24 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver4\), 5)
pv26 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver6\), 5)
pv28 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver8\), 5)
pv46 <- round(getPvalWilcox(tempDown, \pnOver4\, \pnOver6\), 5)
pv48 <- round(getPvalWilcox(tempDown, \pnOver4\, \pnOver8\), 5)
pv68 <- round(getPvalWilcox(tempDown, \pnOver6\, \pnOver8\), 5)
p <- ggplot(tempDown, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)+
annotate(\text\, x = 1, y = -0.5, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
\pv04: \, convPvalue(pv04), \\n\,
\pv06: \, convPvalue(pv06), \\n\,
\pv08: \, convPvalue(pv08), \\n\,
\pv24: \, convPvalue(pv24), \\n\,
\pv26: \, convPvalue(pv26), \\n\,
\pv28: \, convPvalue(pv28), \\n\,
\pv46: \, convPvalue(pv46), \\n\,
\pv48: \, convPvalue(pv48), \\n\,
\pv68: \, convPvalue(pv68), \\n\),
color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_pnGroup_dTAG_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
pv02 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver2\), 5)
pv04 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver4\), 5)
pv06 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver6\), 5)
pv08 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver8\), 5)
pv24 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver4\), 5)
pv26 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver6\), 5)
pv28 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver8\), 5)
pv46 <- round(getPvalWilcox(tempUp, \pnOver4\, \pnOver6\), 5)
pv48 <- round(getPvalWilcox(tempUp, \pnOver4\, \pnOver8\), 5)
pv68 <- round(getPvalWilcox(tempUp, \pnOver6\, \pnOver8\), 5)
p <- ggplot(tempUp, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)+
annotate(\text\, x = 1, y = 0.5, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
\pv04: \, convPvalue(pv04), \\n\,
\pv06: \, convPvalue(pv06), \\n\,
\pv08: \, convPvalue(pv08), \\n\,
\pv24: \, convPvalue(pv24), \\n\,
\pv26: \, convPvalue(pv26), \\n\,
\pv28: \, convPvalue(pv28), \\n\,
\pv46: \, convPvalue(pv46), \\n\,
\pv48: \, convPvalue(pv48), \\n\,
\pv68: \, convPvalue(pv68), \\n\),
color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_pnGroup_dTAG_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### loop score
<!-- rnb-text-end -->
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# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
psOver = ifelse(gene %in% psOver4, \psOver4\,
ifelse(gene %in% psOver3, \psOver3\,
ifelse(gene %in% psOver2, \psOver2\,
ifelse(gene %in% psOver1, \psOver1\,
ifelse(gene %in% psOver0, \psOver0\, NA)))))) %>%
drop_na(psOver)
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```r
```r
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
psOver = ifelse(gene %in% psOver4, \psOver4\,
ifelse(gene %in% psOver3, \psOver3\,
ifelse(gene %in% psOver2, \psOver2\,
ifelse(gene %in% psOver1, \psOver1\,
ifelse(gene %in% psOver0, \psOver0\, NA)))))) %>%
drop_na(psOver)
```
```
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##### log2FC
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-->\n\n```r\nGOfigDir <- here(figDir, \\../GO\\)\n\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\n\nfwrite(GO0.df, here(\\GO_PS_group0.tsv\\), sep = \\\\t\\)\nfwrite(GO1.df, here(\\GO_PS_group1.tsv\\), sep = \\\\t\\)\nfwrite(GO2.df, here(\\GO_PS_group2.tsv\\), sep = \\\\t\\)\nfwrite(GO3.df, here(\\GO_PS_group3.tsv\\), sep = \\\\t\\)\nfwrite(GO4.df, here(\\GO_PS_group4.tsv\\), sep = \\\\t\\)\n\nGO0.df <- fread(here(\\GO_PS_group0.tsv\\))\nGO1.df <- fread(here(\\GO_PS_group1.tsv\\))\nGO2.df <- fread(here(\\GO_PS_group2.tsv\\))\nGO3.df <- fread(here(\\GO_PS_group3.tsv\\))\nGO4.df <- fread(here(\\GO_PS_group4.tsv\\))\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 0\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 3\\) %>%\n  dplyr::arrange(p.adjust)\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 4\\) %>%\n  dplyr::arrange(p.adjust)\n\nsubset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\n\nGOlist <- factor(c(\\GO:0008380\\, \\GO:0006397\\, \\GO:0034470\\, \\GO:0022613\\, \\GO:0016055\\,\n                   \\GO:0061138\\, \\GO:0060562\\, \\GO:0007389\\, \\GO:0060485\\, \\GO:0048638\\, \\GO:0045664\\))\n\ndata <- bind_rows(bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset3), subset4) %>%\n  dplyr::filter(ID %in% GOlist)\n\ndescOrder <- sort(unique(data$Description))[rev(c(10, 4, 5, 9, 11,\n                                                  2, 6,\n                                                  3, 1, 8, 7))]\n\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_ps\\)\nwidth <- panelSize(2.7)*mmToInch\nheight <- panelSize(1.2)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
GOfigDir <- here(figDir, \../GO\)
#####################
GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
fwrite(GO0.df, here(\GO_PS_group0.tsv\), sep = \\t\)
fwrite(GO1.df, here(\GO_PS_group1.tsv\), sep = \\t\)
fwrite(GO2.df, here(\GO_PS_group2.tsv\), sep = \\t\)
fwrite(GO3.df, here(\GO_PS_group3.tsv\), sep = \\t\)
fwrite(GO4.df, here(\GO_PS_group4.tsv\), sep = \\t\)
GO0.df <- fread(here(\GO_PS_group0.tsv\))
GO1.df <- fread(here(\GO_PS_group1.tsv\))
GO2.df <- fread(here(\GO_PS_group2.tsv\))
GO3.df <- fread(here(\GO_PS_group3.tsv\))
GO4.df <- fread(here(\GO_PS_group4.tsv\))
subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 0\) %>%
dplyr::arrange(p.adjust)
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 1\) %>%
dplyr::arrange(p.adjust)
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 2\) %>%
dplyr::arrange(p.adjust)
subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 3\) %>%
dplyr::arrange(p.adjust)
subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 4\) %>%
dplyr::arrange(p.adjust)
subset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
GOlist <- factor(c(\GO:0008380\, \GO:0006397\, \GO:0034470\, \GO:0022613\, \GO:0016055\,
\GO:0061138\, \GO:0060562\, \GO:0007389\, \GO:0060485\, \GO:0048638\, \GO:0045664\))
data <- bind_rows(bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset3), subset4) %>%
dplyr::filter(ID %in% GOlist)
descOrder <- sort(unique(data$Description))[rev(c(10, 4, 5, 9, 11,
2, 6,
3, 1, 8, 7))]
data <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))
p <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM) +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
# limits = c(0, 1),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme_bw() + # Apply theme_bw first, so custom theme settings come after
theme(
panel.background = element_rect(fill = \transparent\), # Override theme_bw panel
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeS, # Ensure size is set for x-axis text
family = fontType,
color = \#000000\,
),
axis.text.y = element_text(
size = fontSizeS, # Ensure size is set for y-axis text
family = fontType,
color = \#000000\,
lineheight = 0.9 # Allows wrapping for y-axis labels to fit into 2 lines
),
axis.line = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \..\, \GO\, \GO_groups_ps\)
width <- panelSize(2.7)*mmToInch
height <- panelSize(1.2)*mmToInch
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nGOfigDir <- here(figDir, \\../GO\\)\n\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\n\nfwrite(GO0.df, here(\\GO_PS_group0.tsv\\), sep = \\\\t\\)\nfwrite(GO1.df, here(\\GO_PS_group1.tsv\\), sep = \\\\t\\)\nfwrite(GO2.df, here(\\GO_PS_group2.tsv\\), sep = \\\\t\\)\nfwrite(GO3.df, here(\\GO_PS_group3.tsv\\), sep = \\\\t\\)\nfwrite(GO4.df, here(\\GO_PS_group4.tsv\\), sep = \\\\t\\)\n\nGO0.df <- fread(here(\\GO_PS_group0.tsv\\))\nGO1.df <- fread(here(\\GO_PS_group1.tsv\\))\nGO2.df <- fread(here(\\GO_PS_group2.tsv\\))\nGO3.df <- fread(here(\\GO_PS_group3.tsv\\))\nGO4.df <- fread(here(\\GO_PS_group4.tsv\\))\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 0\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 3\\) %>%\n  dplyr::arrange(p.adjust)\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 4\\) %>%\n  dplyr::arrange(p.adjust)\n\nsubset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\n\nGOlist <- factor(c(\\GO:0008380\\, \\GO:0006397\\, \\GO:0034470\\, \\GO:0022613\\, \\GO:0016055\\,\n                   \\GO:0061138\\, \\GO:0060562\\, \\GO:0007389\\, \\GO:0060485\\, \\GO:0048638\\, \\GO:0045664\\))\n\ndata <- bind_rows(bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset3), subset4) %>%\n  dplyr::filter(ID %in% GOlist)\n\ndescOrder <- sort(unique(data$Description))[rev(c(10, 4, 5, 9, 11,\n                                                  2, 6,\n                                                  3, 1, 8, 7))]\n\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_ps\\)\nwidth <- panelSize(2.7)*mmToInch\nheight <- panelSize(1.2)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
GOfigDir <- here(figDir, \../GO\)
#####################
GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
fwrite(GO0.df, here(\GO_PS_group0.tsv\), sep = \\t\)
fwrite(GO1.df, here(\GO_PS_group1.tsv\), sep = \\t\)
fwrite(GO2.df, here(\GO_PS_group2.tsv\), sep = \\t\)
fwrite(GO3.df, here(\GO_PS_group3.tsv\), sep = \\t\)
fwrite(GO4.df, here(\GO_PS_group4.tsv\), sep = \\t\)
GO0.df <- fread(here(\GO_PS_group0.tsv\))
GO1.df <- fread(here(\GO_PS_group1.tsv\))
GO2.df <- fread(here(\GO_PS_group2.tsv\))
GO3.df <- fread(here(\GO_PS_group3.tsv\))
GO4.df <- fread(here(\GO_PS_group4.tsv\))
subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 0\) %>%
dplyr::arrange(p.adjust)
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 1\) %>%
dplyr::arrange(p.adjust)
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 2\) %>%
dplyr::arrange(p.adjust)
subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 3\) %>%
dplyr::arrange(p.adjust)
subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 4\) %>%
dplyr::arrange(p.adjust)
subset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
GOlist <- factor(c(\GO:0008380\, \GO:0006397\, \GO:0034470\, \GO:0022613\, \GO:0016055\,
\GO:0061138\, \GO:0060562\, \GO:0007389\, \GO:0060485\, \GO:0048638\, \GO:0045664\))
data <- bind_rows(bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset3), subset4) %>%
dplyr::filter(ID %in% GOlist)
descOrder <- sort(unique(data$Description))[rev(c(10, 4, 5, 9, 11,
2, 6,
3, 1, 8, 7))]
data <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))
p <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM) +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
# limits = c(0, 1),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme_bw() + # Apply theme_bw first, so custom theme settings come after
theme(
panel.background = element_rect(fill = \transparent\), # Override theme_bw panel
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeS, # Ensure size is set for x-axis text
family = fontType,
color = \#000000\,
),
axis.text.y = element_text(
size = fontSizeS, # Ensure size is set for y-axis text
family = fontType,
color = \#000000\,
lineheight = 0.9 # Allows wrapping for y-axis labels to fit into 2 lines
),
axis.line = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \..\, \GO\, \GO_groups_ps\)
width <- panelSize(2.7)*mmToInch
height <- panelSize(1.2)*mmToInch
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Grouping with P-S number
##### Grouping
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nps01 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(temp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) + \n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\Average Δ loop score\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) +\n    scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  coord_cartesian(ylim = c(-0.5, 0.1))\n\n\n\nfileName <- paste0(\\diffScore_barplot_psGroup_dTAG_vs_DMSO_2\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) +
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \Average Δ loop score\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) +
scale_fill_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)) +
coord_cartesian(ylim = c(-0.5, 0.1))
fileName <- paste0(\diffScore_barplot_psGroup_dTAG_vs_DMSO_2\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nps01 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(temp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) + \n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\Average Δ loop score\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) +\n    scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  coord_cartesian(ylim = c(-0.5, 0.1))\n\n\n\nfileName <- paste0(\\diffScore_barplot_psGroup_dTAG_vs_DMSO_2\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) +
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \Average Δ loop score\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) +
scale_fill_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)) +
coord_cartesian(ylim = c(-0.5, 0.1))
fileName <- paste0(\diffScore_barplot_psGroup_dTAG_vs_DMSO_2\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### GO for each group
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\n\nks_result1 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver3\\) %>% pull(absLog2FC)\n)\nks_result2 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver2\\) %>% pull(absLog2FC)\n)\nks_result3 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver1\\) %>% pull(absLog2FC)\n)\nks_result4 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver0\\) %>% pull(absLog2FC)\n)\n\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_psGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
temp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))
ks_result1 <- ks.test(
temp %>% dplyr::filter(psOver == \psOver4\) %>% pull(absLog2FC),
temp %>% dplyr::filter(psOver == \psOver3\) %>% pull(absLog2FC)
)
ks_result2 <- ks.test(
temp %>% dplyr::filter(psOver == \psOver4\) %>% pull(absLog2FC),
temp %>% dplyr::filter(psOver == \psOver2\) %>% pull(absLog2FC)
)
ks_result3 <- ks.test(
temp %>% dplyr::filter(psOver == \psOver4\) %>% pull(absLog2FC),
temp %>% dplyr::filter(psOver == \psOver1\) %>% pull(absLog2FC)
)
ks_result4 <- ks.test(
temp %>% dplyr::filter(psOver == \psOver4\) %>% pull(absLog2FC),
temp %>% dplyr::filter(psOver == \psOver0\) %>% pull(absLog2FC)
)
p <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +
scale_color_manual(values = (c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\log2FC_cdf_psGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_psGroup_dTAG_vs_DMSO_down\)
width <- panelSize(1.2)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
p <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_psGroup_dTAG_vs_DMSO_up\)
width <- panelSize(1.18)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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 -->\n\n```r\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\n\nks_result1 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver3\\) %>% pull(absLog2FC)\n)\nks_result2 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver2\\) %>% pull(absLog2FC)\n)\nks_result3 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver1\\) %>% pull(absLog2FC)\n)\nks_result4 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver0\\) %>% pull(absLog2FC)\n)\n\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_psGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\n\nks_result1 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver3\\) %>% pull(absLog2FC)\n)\nks_result2 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver2\\) %>% pull(absLog2FC)\n)\nks_result3 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver1\\) %>% pull(absLog2FC)\n)\nks_result4 <- ks.test(\n  temp %>% dplyr::filter(psOver == \\psOver4\\) %>% pull(absLog2FC),\n  temp %>% dplyr::filter(psOver == \\psOver0\\) %>% pull(absLog2FC)\n)\n\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_psGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
temp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))
ks_result1 <- ks.test(
temp %>% dplyr::filter(psOver == \psOver4\) %>% pull(absLog2FC),
temp %>% dplyr::filter(psOver == \psOver3\) %>% pull(absLog2FC)
)
ks_result2 <- ks.test(
temp %>% dplyr::filter(psOver == \psOver4\) %>% pull(absLog2FC),
temp %>% dplyr::filter(psOver == \psOver2\) %>% pull(absLog2FC)
)
ks_result3 <- ks.test(
temp %>% dplyr::filter(psOver == \psOver4\) %>% pull(absLog2FC),
temp %>% dplyr::filter(psOver == \psOver1\) %>% pull(absLog2FC)
)
ks_result4 <- ks.test(
temp %>% dplyr::filter(psOver == \psOver4\) %>% pull(absLog2FC),
temp %>% dplyr::filter(psOver == \psOver0\) %>% pull(absLog2FC)
)
p <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +
scale_color_manual(values = (c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\log2FC_cdf_psGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_psGroup_dTAG_vs_DMSO_down\)
width <- panelSize(1.2)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
p <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_psGroup_dTAG_vs_DMSO_up\)
width <- panelSize(1.18)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### loop score
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
psOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
psOver = ifelse(gene %in% psOver4, \psOver4\,
ifelse(gene %in% psOver3, \psOver3\,
ifelse(gene %in% psOver2, \psOver2\,
ifelse(gene %in% psOver1, \psOver1\,
ifelse(gene %in% psOver0, \psOver0\, NA)))))) %>%
drop_na(psOver)
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```r
```r
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
psOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
psOver = ifelse(gene %in% psOver4, \psOver4\,
ifelse(gene %in% psOver3, \psOver3\,
ifelse(gene %in% psOver2, \psOver2\,
ifelse(gene %in% psOver1, \psOver1\,
ifelse(gene %in% psOver0, \psOver0\, NA)))))) %>%
drop_na(psOver)
```
```
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##### log2FC
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-->\n\n```r\nGOfigDir <- here(figDir, \\../GO\\)\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\n\n# fwrite(GO0.df, here(\\GO_PE_group0.tsv\\), sep = \\\\t\\)\n# fwrite(GO1.df, here(\\GO_PE_group1.tsv\\), sep = \\\\t\\)\n# fwrite(GO2.df, here(\\GO_PE_group2.tsv\\), sep = \\\\t\\)\n# fwrite(GO3.df, here(\\GO_PE_group3.tsv\\), sep = \\\\t\\)\n# fwrite(GO4.df, here(\\GO_PE_group4.tsv\\), sep = \\\\t\\)\n\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 0\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 3\\) %>%\n  dplyr::arrange(p.adjust)\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 4\\) %>%\n  dplyr::arrange(p.adjust)\n\nsubset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\nGOlist <- factor(c(\\GO:0008380\\, \\GO:0006397\\, \\GO:0034470\\, \\GO:0022613\\, \\GO:0016055\\,\n                   \\GO:0061138\\, \\GO:0060562\\, \\GO:0007389\\, \\GO:0060485\\, \\GO:0048638\\, \\GO:0045664\\,\n                   \\GO:0040029\\))\n\ndata <- bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset4) %>%\n  dplyr::filter(ID %in% GOlist)\n\ndescOrder <- sort(unique(data$Description))[rev(c(11, 5, 6, 10, 12,\n                                                  1,\n                                                  3, 7, 4, 2, 9, 8))]\n\nempty_row3 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 3\\                   # Group to add as empty column\n)\n\nempty_row4 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 4\\                   # Group to add as empty column\n)\n\n# Append the empty row to your dataset\ndata <- rbind(data, empty_row3, empty_row4)\n\n\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pe\\)\nwidth <- panelSize(2.65)*mmToInch\nheight <- panelSize(1.3)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
GOfigDir <- here(figDir, \../GO\)
#####################
GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
# fwrite(GO0.df, here(\GO_PE_group0.tsv\), sep = \\t\)
# fwrite(GO1.df, here(\GO_PE_group1.tsv\), sep = \\t\)
# fwrite(GO2.df, here(\GO_PE_group2.tsv\), sep = \\t\)
# fwrite(GO3.df, here(\GO_PE_group3.tsv\), sep = \\t\)
# fwrite(GO4.df, here(\GO_PE_group4.tsv\), sep = \\t\)
subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 0\) %>%
dplyr::arrange(p.adjust)
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 1\) %>%
dplyr::arrange(p.adjust)
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 2\) %>%
dplyr::arrange(p.adjust)
subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 3\) %>%
dplyr::arrange(p.adjust)
subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 4\) %>%
dplyr::arrange(p.adjust)
subset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
GOlist <- factor(c(\GO:0008380\, \GO:0006397\, \GO:0034470\, \GO:0022613\, \GO:0016055\,
\GO:0061138\, \GO:0060562\, \GO:0007389\, \GO:0060485\, \GO:0048638\, \GO:0045664\,
\GO:0040029\))
data <- bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset4) %>%
dplyr::filter(ID %in% GOlist)
descOrder <- sort(unique(data$Description))[rev(c(11, 5, 6, 10, 12,
1,
3, 7, 4, 2, 9, 8))]
empty_row3 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 3\ # Group to add as empty column
)
empty_row4 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 4\ # Group to add as empty column
)
# Append the empty row to your dataset
data <- rbind(data, empty_row3, empty_row4)
data <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))
p <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM) +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
# limits = c(0, 1),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme_bw() + # Apply theme_bw first, so custom theme settings come after
theme(
panel.background = element_rect(fill = \transparent\), # Override theme_bw panel
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeS, # Ensure size is set for x-axis text
family = fontType,
color = \#000000\,
),
axis.text.y = element_text(
size = fontSizeS, # Ensure size is set for y-axis text
family = fontType,
color = \#000000\,
lineheight = 0.9 # Allows wrapping for y-axis labels to fit into 2 lines
),
axis.line = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \..\, \GO\, \GO_groups_pe\)
width <- panelSize(2.65)*mmToInch
height <- panelSize(1.3)*mmToInch
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nGOfigDir <- here(figDir, \\../GO\\)\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\n\n# fwrite(GO0.df, here(\\GO_PE_group0.tsv\\), sep = \\\\t\\)\n# fwrite(GO1.df, here(\\GO_PE_group1.tsv\\), sep = \\\\t\\)\n# fwrite(GO2.df, here(\\GO_PE_group2.tsv\\), sep = \\\\t\\)\n# fwrite(GO3.df, here(\\GO_PE_group3.tsv\\), sep = \\\\t\\)\n# fwrite(GO4.df, here(\\GO_PE_group4.tsv\\), sep = \\\\t\\)\n\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 0\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 3\\) %>%\n  dplyr::arrange(p.adjust)\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 4\\) %>%\n  dplyr::arrange(p.adjust)\n\nsubset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\nGOlist <- factor(c(\\GO:0008380\\, \\GO:0006397\\, \\GO:0034470\\, \\GO:0022613\\, \\GO:0016055\\,\n                   \\GO:0061138\\, \\GO:0060562\\, \\GO:0007389\\, \\GO:0060485\\, \\GO:0048638\\, \\GO:0045664\\,\n                   \\GO:0040029\\))\n\ndata <- bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset4) %>%\n  dplyr::filter(ID %in% GOlist)\n\ndescOrder <- sort(unique(data$Description))[rev(c(11, 5, 6, 10, 12,\n                                                  1,\n                                                  3, 7, 4, 2, 9, 8))]\n\nempty_row3 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 3\\                   # Group to add as empty column\n)\n\nempty_row4 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 4\\                   # Group to add as empty column\n)\n\n# Append the empty row to your dataset\ndata <- rbind(data, empty_row3, empty_row4)\n\n\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pe\\)\nwidth <- panelSize(2.65)*mmToInch\nheight <- panelSize(1.3)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
GOfigDir <- here(figDir, \../GO\)
#####################
GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
# fwrite(GO0.df, here(\GO_PE_group0.tsv\), sep = \\t\)
# fwrite(GO1.df, here(\GO_PE_group1.tsv\), sep = \\t\)
# fwrite(GO2.df, here(\GO_PE_group2.tsv\), sep = \\t\)
# fwrite(GO3.df, here(\GO_PE_group3.tsv\), sep = \\t\)
# fwrite(GO4.df, here(\GO_PE_group4.tsv\), sep = \\t\)
subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 0\) %>%
dplyr::arrange(p.adjust)
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 1\) %>%
dplyr::arrange(p.adjust)
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 2\) %>%
dplyr::arrange(p.adjust)
subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 3\) %>%
dplyr::arrange(p.adjust)
subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 4\) %>%
dplyr::arrange(p.adjust)
subset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
GOlist <- factor(c(\GO:0008380\, \GO:0006397\, \GO:0034470\, \GO:0022613\, \GO:0016055\,
\GO:0061138\, \GO:0060562\, \GO:0007389\, \GO:0060485\, \GO:0048638\, \GO:0045664\,
\GO:0040029\))
data <- bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset4) %>%
dplyr::filter(ID %in% GOlist)
descOrder <- sort(unique(data$Description))[rev(c(11, 5, 6, 10, 12,
1,
3, 7, 4, 2, 9, 8))]
empty_row3 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 3\ # Group to add as empty column
)
empty_row4 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 4\ # Group to add as empty column
)
# Append the empty row to your dataset
data <- rbind(data, empty_row3, empty_row4)
data <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))
p <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM) +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
# limits = c(0, 1),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme_bw() + # Apply theme_bw first, so custom theme settings come after
theme(
panel.background = element_rect(fill = \transparent\), # Override theme_bw panel
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeS, # Ensure size is set for x-axis text
family = fontType,
color = \#000000\,
),
axis.text.y = element_text(
size = fontSizeS, # Ensure size is set for y-axis text
family = fontType,
color = \#000000\,
lineheight = 0.9 # Allows wrapping for y-axis labels to fit into 2 lines
),
axis.line = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \..\, \GO\, \GO_groups_pe\)
width <- panelSize(2.65)*mmToInch
height <- panelSize(1.3)*mmToInch
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Grouping with P-E number
##### Grouping
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nps01 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(temp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) + \n    scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\Average Δ loop score\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) +\n  coord_cartesian(ylim = c(-0.5, 0.1))\n\n\nfileName <- paste0(\\diffScore_barplot_peGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) +
scale_fill_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)) +
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \Average Δ loop score\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) +
coord_cartesian(ylim = c(-0.5, 0.1))
fileName <- paste0(\diffScore_barplot_peGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nps01 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(temp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) + \n    scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\Average Δ loop score\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) +\n  coord_cartesian(ylim = c(-0.5, 0.1))\n\n\nfileName <- paste0(\\diffScore_barplot_peGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) +
scale_fill_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)) +
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \Average Δ loop score\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) +
coord_cartesian(ylim = c(-0.5, 0.1))
fileName <- paste0(\diffScore_barplot_peGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### GO for each group
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_peGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
temp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))
p <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +
scale_color_manual(values = (c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\log2FC_cdf_peGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_peGroup_dTAG_vs_DMSO_down\)
width <- panelSize(1.2)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
p <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_peGroup_dTAG_vs_DMSO_up\)
width <- panelSize(1.18)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_peGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_peGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
temp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))
p <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +
scale_color_manual(values = (c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\log2FC_cdf_peGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_peGroup_dTAG_vs_DMSO_down\)
width <- panelSize(1.2)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
p <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_peGroup_dTAG_vs_DMSO_up\)
width <- panelSize(1.18)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
##### loop score
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<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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ICAgICAgICAgICAgICAgaWZlbHNlKGdlbmUgJWluJSBwc092ZXIzLCBcXHBzT3ZlcjNcXCxcbiAgICAgICAgICAgICAgICAgICAgICAgICBpZmVsc2UoZ2VuZSAlaW4lIHBzT3ZlcjIsIFxccHNPdmVyMlxcLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBpZmVsc2UoZ2VuZSAlaW4lIHBzT3ZlcjEsIFxccHNPdmVyMVxcLFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgaWZlbHNlKGdlbmUgJWluJSBwc092ZXIwLCBcXHBzT3ZlcjBcXCwgTkEpKSkpKSkgJT4lXG4gIGRyb3BfbmEocHNPdmVyKVxuXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
psOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
psOver = ifelse(gene %in% psOver4, \psOver4\,
ifelse(gene %in% psOver3, \psOver3\,
ifelse(gene %in% psOver2, \psOver2\,
ifelse(gene %in% psOver1, \psOver1\,
ifelse(gene %in% psOver0, \psOver0\, NA)))))) %>%
drop_na(psOver)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
psOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
psOver = ifelse(gene %in% psOver4, \psOver4\,
ifelse(gene %in% psOver3, \psOver3\,
ifelse(gene %in% psOver2, \psOver2\,
ifelse(gene %in% psOver1, \psOver1\,
ifelse(gene %in% psOver0, \psOver0\, NA)))))) %>%
drop_na(psOver)
```
```
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##### log2FC
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-->\n\n```r\nGOfigDir <- here(figDir, \\../GO\\)\n\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\n# \n# fwrite(GO0.df, here(\\GO_PP_group0.tsv\\), sep = \\\\t\\)\n# fwrite(GO1.df, here(\\GO_PP_group1.tsv\\), sep = \\\\t\\)\n# fwrite(GO2.df, here(\\GO_PP_group2.tsv\\), sep = \\\\t\\)\n# fwrite(GO3.df, here(\\GO_PP_group3.tsv\\), sep = \\\\t\\)\n# fwrite(GO4.df, here(\\GO_PP_group4.tsv\\), sep = \\\\t\\)\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 0\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 3\\) %>%\n  dplyr::arrange(p.adjust)\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 4\\) %>%\n  dplyr::arrange(p.adjust)\n\nsubset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\nGOlist <- factor(c(\\GO:0008380\\, \\GO:0006397\\, \\GO:0034470\\, \\GO:0022613\\, \\GO:0016055\\,\n                   \\GO:0061138\\, \\GO:0060562\\, \\GO:0007389\\, \\GO:0060485\\, \\GO:0048638\\, \\GO:0045664\\))\n\ndata <- bind_rows(subset0, subset1) %>%\n  dplyr::filter(ID %in% GOlist)\n\n\n\nempty_row2 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 2\\                   # Group to add as empty column\n)\n\nempty_row3 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 3\\                   # Group to add as empty column\n)\n\nempty_row4 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 4\\                   # Group to add as empty column\n)\n\n# Append the empty row to your dataset\ndata <- rbind(data, empty_row2, empty_row3, empty_row4)\ndescOrder <- sort(unique(data$Description))[rev(c(12,\n                                                  4, 5, 9, 11, 10, \n                                                  2, 6, 3, 1, 8, 7))]\ndata <- data %>% dplyr::filter(Description %in% descOrder)\n\n\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pp\\)\nwidth <- panelSize(2.65)*mmToInch\nheight <- panelSize(1.2)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
GOfigDir <- here(figDir, \../GO\)
#####################
GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
#
# fwrite(GO0.df, here(\GO_PP_group0.tsv\), sep = \\t\)
# fwrite(GO1.df, here(\GO_PP_group1.tsv\), sep = \\t\)
# fwrite(GO2.df, here(\GO_PP_group2.tsv\), sep = \\t\)
# fwrite(GO3.df, here(\GO_PP_group3.tsv\), sep = \\t\)
# fwrite(GO4.df, here(\GO_PP_group4.tsv\), sep = \\t\)
subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 0\) %>%
dplyr::arrange(p.adjust)
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 1\) %>%
dplyr::arrange(p.adjust)
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 2\) %>%
dplyr::arrange(p.adjust)
subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 3\) %>%
dplyr::arrange(p.adjust)
subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 4\) %>%
dplyr::arrange(p.adjust)
subset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
GOlist <- factor(c(\GO:0008380\, \GO:0006397\, \GO:0034470\, \GO:0022613\, \GO:0016055\,
\GO:0061138\, \GO:0060562\, \GO:0007389\, \GO:0060485\, \GO:0048638\, \GO:0045664\))
data <- bind_rows(subset0, subset1) %>%
dplyr::filter(ID %in% GOlist)
empty_row2 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 2\ # Group to add as empty column
)
empty_row3 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 3\ # Group to add as empty column
)
empty_row4 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 4\ # Group to add as empty column
)
# Append the empty row to your dataset
data <- rbind(data, empty_row2, empty_row3, empty_row4)
descOrder <- sort(unique(data$Description))[rev(c(12,
4, 5, 9, 11, 10,
2, 6, 3, 1, 8, 7))]
data <- data %>% dplyr::filter(Description %in% descOrder)
data <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))
p <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM) +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
# limits = c(0, 1),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme_bw() + # Apply theme_bw first, so custom theme settings come after
theme(
panel.background = element_rect(fill = \transparent\), # Override theme_bw panel
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeS, # Ensure size is set for x-axis text
family = fontType,
color = \#000000\,
),
axis.text.y = element_text(
size = fontSizeS, # Ensure size is set for y-axis text
family = fontType,
color = \#000000\,
lineheight = 0.9 # Allows wrapping for y-axis labels to fit into 2 lines
),
axis.line = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \..\, \GO\, \GO_groups_pp\)
width <- panelSize(2.65)*mmToInch
height <- panelSize(1.2)*mmToInch
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nGOfigDir <- here(figDir, \\../GO\\)\n\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\n# \n# fwrite(GO0.df, here(\\GO_PP_group0.tsv\\), sep = \\\\t\\)\n# fwrite(GO1.df, here(\\GO_PP_group1.tsv\\), sep = \\\\t\\)\n# fwrite(GO2.df, here(\\GO_PP_group2.tsv\\), sep = \\\\t\\)\n# fwrite(GO3.df, here(\\GO_PP_group3.tsv\\), sep = \\\\t\\)\n# fwrite(GO4.df, here(\\GO_PP_group4.tsv\\), sep = \\\\t\\)\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 0\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 3\\) %>%\n  dplyr::arrange(p.adjust)\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 4\\) %>%\n  dplyr::arrange(p.adjust)\n\nsubset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\nGOlist <- factor(c(\\GO:0008380\\, \\GO:0006397\\, \\GO:0034470\\, \\GO:0022613\\, \\GO:0016055\\,\n                   \\GO:0061138\\, \\GO:0060562\\, \\GO:0007389\\, \\GO:0060485\\, \\GO:0048638\\, \\GO:0045664\\))\n\ndata <- bind_rows(subset0, subset1) %>%\n  dplyr::filter(ID %in% GOlist)\n\n\n\nempty_row2 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 2\\                   # Group to add as empty column\n)\n\nempty_row3 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 3\\                   # Group to add as empty column\n)\n\nempty_row4 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 4\\                   # Group to add as empty column\n)\n\n# Append the empty row to your dataset\ndata <- rbind(data, empty_row2, empty_row3, empty_row4)\ndescOrder <- sort(unique(data$Description))[rev(c(12,\n                                                  4, 5, 9, 11, 10, \n                                                  2, 6, 3, 1, 8, 7))]\ndata <- data %>% dplyr::filter(Description %in% descOrder)\n\n\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pp\\)\nwidth <- panelSize(2.65)*mmToInch\nheight <- panelSize(1.2)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nGOfigDir <- here(figDir, \\../GO\\)\n\n#####################\nGO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\nGO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\, readable = TRUE))\n# \n# fwrite(GO0.df, here(\\GO_PP_group0.tsv\\), sep = \\\\t\\)\n# fwrite(GO1.df, here(\\GO_PP_group1.tsv\\), sep = \\\\t\\)\n# fwrite(GO2.df, here(\\GO_PP_group2.tsv\\), sep = \\\\t\\)\n# fwrite(GO3.df, here(\\GO_PP_group3.tsv\\), sep = \\\\t\\)\n# fwrite(GO4.df, here(\\GO_PP_group4.tsv\\), sep = \\\\t\\)\n\nsubset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 0\\) %>%\n  dplyr::arrange(p.adjust)\nsubset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 1\\) %>%\n  dplyr::arrange(p.adjust)\nsubset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 2\\) %>%\n  dplyr::arrange(p.adjust)\nsubset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 3\\) %>%\n  dplyr::arrange(p.adjust)\nsubset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\Group 4\\) %>%\n  dplyr::arrange(p.adjust)\n\nsubset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\nsubset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \\/\\), function(x) as.numeric(x[1]) / as.numeric(x[2]))\n\nGOlist <- factor(c(\\GO:0008380\\, \\GO:0006397\\, \\GO:0034470\\, \\GO:0022613\\, \\GO:0016055\\,\n                   \\GO:0061138\\, \\GO:0060562\\, \\GO:0007389\\, \\GO:0060485\\, \\GO:0048638\\, \\GO:0045664\\))\n\ndata <- bind_rows(subset0, subset1) %>%\n  dplyr::filter(ID %in% GOlist)\n\n\n\nempty_row2 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 2\\                   # Group to add as empty column\n)\n\nempty_row3 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 3\\                   # Group to add as empty column\n)\n\nempty_row4 <- data.frame(\n  ID = NA,                             # No specific ID\n  Description = NA,                    # No description\n  GeneRatio = NA,                      # No gene ratio\n  p.adjust = NA,                       # No p.adjust value\n  group = \\Group 4\\                   # Group to add as empty column\n)\n\n# Append the empty row to your dataset\ndata <- rbind(data, empty_row2, empty_row3, empty_row4)\ndescOrder <- sort(unique(data$Description))[rev(c(12,\n                                                  4, 5, 9, 11, 10, \n                                                  2, 6, 3, 1, 8, 7))]\ndata <- data %>% dplyr::filter(Description %in% descOrder)\n\n\ndata <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))\np <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) + \n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM) + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      # limits = c(0, 1),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL) +\n  theme_bw() +  # Apply theme_bw first, so custom theme settings come after\n  theme(\n    panel.background = element_rect(fill = \\transparent\\),  # Override theme_bw panel\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      size = fontSizeS,         # Ensure size is set for x-axis text\n      family = fontType,\n      color = \\#000000\\,\n    ),\n    axis.text.y = element_text(\n      size = fontSizeS,         # Ensure size is set for y-axis text\n      family = fontType,\n      color = \\#000000\\,\n      lineheight = 0.9          # Allows wrapping for y-axis labels to fit into 2 lines\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick * mmToLineUnit,\n      lineend = \\square\\\n    ),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pp\\)\nwidth <- panelSize(2.65)*mmToInch\nheight <- panelSize(1.2)*mmToInch\nsvglite(paste0(fileName, \\.svg\\), height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
GOfigDir <- here(figDir, \../GO\)
#####################
GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\, readable = TRUE))
#
# fwrite(GO0.df, here(\GO_PP_group0.tsv\), sep = \\t\)
# fwrite(GO1.df, here(\GO_PP_group1.tsv\), sep = \\t\)
# fwrite(GO2.df, here(\GO_PP_group2.tsv\), sep = \\t\)
# fwrite(GO3.df, here(\GO_PP_group3.tsv\), sep = \\t\)
# fwrite(GO4.df, here(\GO_PP_group4.tsv\), sep = \\t\)
subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 0\) %>%
dplyr::arrange(p.adjust)
subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 1\) %>%
dplyr::arrange(p.adjust)
subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 2\) %>%
dplyr::arrange(p.adjust)
subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 3\) %>%
dplyr::arrange(p.adjust)
subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \Group 4\) %>%
dplyr::arrange(p.adjust)
subset0$GeneRatio <- sapply(strsplit(subset0$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset1$GeneRatio <- sapply(strsplit(subset1$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset2$GeneRatio <- sapply(strsplit(subset2$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset3$GeneRatio <- sapply(strsplit(subset3$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
subset4$GeneRatio <- sapply(strsplit(subset4$GeneRatio, \/\), function(x) as.numeric(x[1]) / as.numeric(x[2]))
GOlist <- factor(c(\GO:0008380\, \GO:0006397\, \GO:0034470\, \GO:0022613\, \GO:0016055\,
\GO:0061138\, \GO:0060562\, \GO:0007389\, \GO:0060485\, \GO:0048638\, \GO:0045664\))
data <- bind_rows(subset0, subset1) %>%
dplyr::filter(ID %in% GOlist)
empty_row2 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 2\ # Group to add as empty column
)
empty_row3 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 3\ # Group to add as empty column
)
empty_row4 <- data.frame(
ID = NA, # No specific ID
Description = NA, # No description
GeneRatio = NA, # No gene ratio
p.adjust = NA, # No p.adjust value
group = \Group 4\ # Group to add as empty column
)
# Append the empty row to your dataset
data <- rbind(data, empty_row2, empty_row3, empty_row4)
descOrder <- sort(unique(data$Description))[rev(c(12,
4, 5, 9, 11, 10,
2, 6, 3, 1, 8, 7))]
data <- data %>% dplyr::filter(Description %in% descOrder)
data <- data %>% dplyr::rowwise() %>% dplyr::mutate(pValueLog = min(-log10(p.adjust), pValueLogMax))
p <- ggplot(data, aes(x = group, y = factor(Description, levels = descOrder), size = pValueLog, fill = GeneRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM) +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
# limits = c(0, 1),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme_bw() + # Apply theme_bw first, so custom theme settings come after
theme(
panel.background = element_rect(fill = \transparent\), # Override theme_bw panel
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeS, # Ensure size is set for x-axis text
family = fontType,
color = \#000000\,
),
axis.text.y = element_text(
size = fontSizeS, # Ensure size is set for y-axis text
family = fontType,
color = \#000000\,
lineheight = 0.9 # Allows wrapping for y-axis labels to fit into 2 lines
),
axis.line = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick * mmToLineUnit,
lineend = \square\
),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \..\, \GO\, \GO_groups_pp\)
width <- panelSize(2.65)*mmToInch
height <- panelSize(1.2)*mmToInch
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Grouping with P-P number
##### Grouping
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nps01 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(temp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver4\\), 5)\n\np <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) + \n    scale_fill_manual(values = rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\Average Δ loop score\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) +\n  coord_cartesian(ylim = c(-0.5, 0.1))\n\n\nfileName <- paste0(\\diffScore_barplot_ppGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) +
scale_fill_manual(values = rev(c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \Average Δ loop score\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) +
coord_cartesian(ylim = c(-0.5, 0.1))
fileName <- paste0(\diffScore_barplot_ppGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nps01 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(temp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(temp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(temp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(temp, \\psOver0\\, \\psOver4\\), 5)\n\np <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) + \n    scale_fill_manual(values = rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\Average Δ loop score\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) +\n  coord_cartesian(ylim = c(-0.5, 0.1))\n\n\nfileName <- paste0(\\diffScore_barplot_ppGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) +
scale_fill_manual(values = rev(c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \Average Δ loop score\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) +
coord_cartesian(ylim = c(-0.5, 0.1))
fileName <- paste0(\diffScore_barplot_ppGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### GO for each group
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_ppGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
temp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))
p <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +
scale_color_manual(values = (rev(c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\log2FC_cdf_ppGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_ppGroup_dTAG_vs_DMSO_down\)
width <- panelSize(1.2)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
p <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_ppGroup_dTAG_vs_DMSO_up\)
width <- panelSize(1.18)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_ppGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ntemp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))\np <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +\nscale_color_manual(values = (rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\nfileName <- paste0(\\log2FC_cdf_ppGroup_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n#\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n  distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n \nps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n\nps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_dTAG_vs_DMSO_down\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \nps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\nps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\nps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\nps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\nps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\nps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\nps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n\n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + \n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) + # Five shades of grey\n  geom_violin(aes(fill = psOver), \n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\log2(fold change)\\) +\n  stat_summary(\n    aes(group = psOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_dTAG_vs_DMSO_up\\)\nwidth <- panelSize(1.18)*mmToInch\nheight <- panelSize(1)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
temp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))
p <- ggplot(temp, aes(x = absLog2FC, color = psOver)) +
scale_color_manual(values = (rev(c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\log2FC_cdf_ppGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_ppGroup_dTAG_vs_DMSO_down\)
width <- panelSize(1.2)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
p <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) + # Five shades of grey
geom_violin(aes(fill = psOver),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \log2(fold change)\) +
stat_summary(
aes(group = psOver), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_ppGroup_dTAG_vs_DMSO_up\)
width <- panelSize(1.18)*mmToInch
height <- panelSize(1)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### loop score
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
############
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
# temp2
#calculating diff score and log2fc distribution based on p-n numbers
temp2 <- readRDS(here(resultDir, \gene_loop_link_A485.rds\))
pnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene
pnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene
pnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene
pnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene
pnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
pnOver = ifelse(gene %in% pnOver8, \pnOver8\,
ifelse(gene %in% pnOver6, \pnOver6\,
ifelse(gene %in% pnOver4, \pnOver4\,
ifelse(gene %in% pnOver2, \pnOver2\,
ifelse(gene %in% pnOver0, \pnOver0\, NA)))))) %>%
drop_na(pnOver)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
############
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
# temp2
#calculating diff score and log2fc distribution based on p-n numbers
temp2 <- readRDS(here(resultDir, \gene_loop_link_A485.rds\))
pnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene
pnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene
pnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene
pnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene
pnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
pnOver = ifelse(gene %in% pnOver8, \pnOver8\,
ifelse(gene %in% pnOver6, \pnOver6\,
ifelse(gene %in% pnOver4, \pnOver4\,
ifelse(gene %in% pnOver2, \pnOver2\,
ifelse(gene %in% pnOver0, \pnOver0\, NA)))))) %>%
drop_na(pnOver)
```
```
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##### log2FC
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KGlmIG5vdCByZW1vdmVkKVxuIyAgICAgICAgIGxlZ2VuZC50ZXh0ID0gZWxlbWVudF90ZXh0KHNpemUgPSA2KSwgIyBTZXQgbGVnZW5kIHRleHQgc2l6ZVxuIyAgICAgICAgIGxlZ2VuZC50aXRsZSA9IGVsZW1lbnRfdGV4dChzaXplID0gNikpIFxuIyBcbiMgZmlsZU5hbWUgPC0gaGVyZShmaWdEaXIsIFwiLi5cIiwgXCJHT1wiLCBcIkdPX2dyb3Vwc19wblwiKVxuIyBoZWlnaHQgPSAyXG4jIHdpZHRoID0gMy4zXG4jIHN2Z2xpdGUocGFzdGUwKGZpbGVOYW1lLCBcIi5zdmdcIiksIGhlaWdodCA9IGhlaWdodCwgd2lkdGggPSB3aWR0aClcbiMgcHJpbnQocClcbiMgZGV2Lm9mZigpXG5gYGAifQ== -->\n\n```r\n# GOfigDir <- here(figDir, \\../GO\\)\n# getGO(\\pnOver8\\, GOfigDir, pnOver8)\n# getGO(\\pnOver6\\, GOfigDir, pnOver6)\n# getGO(\\pnOver4\\, GOfigDir, pnOver4)\n# getGO(\\pnOver2\\, GOfigDir, pnOver2)\n# getGO(\\pnOver0\\, GOfigDir, pnOver0)\n# \n# #####################\n# GO0.df <- as.data.frame(enrichGO(gene = pnOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO2.df <- as.data.frame(enrichGO(gene = pnOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO4.df <- as.data.frame(enrichGO(gene = pnOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO6.df <- as.data.frame(enrichGO(gene = pnOver6, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO8.df <- as.data.frame(enrichGO(gene = pnOver8, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# \n# \n# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver0\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver4\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset6 <- GO6.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver6\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset8 <- GO8.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver8\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# \n# GOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n#                    \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n#                    \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\))\n# \n# data <- bind_rows(bind_rows(bind_rows(subset0, subset2), subset4), subset8) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pn\\)\n# height = 2\n# width = 3.3\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# GOfigDir <- here(figDir, \../GO\)
# getGO(\pnOver8\, GOfigDir, pnOver8)
# getGO(\pnOver6\, GOfigDir, pnOver6)
# getGO(\pnOver4\, GOfigDir, pnOver4)
# getGO(\pnOver2\, GOfigDir, pnOver2)
# getGO(\pnOver0\, GOfigDir, pnOver0)
#
# #####################
# GO0.df <- as.data.frame(enrichGO(gene = pnOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO2.df <- as.data.frame(enrichGO(gene = pnOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO4.df <- as.data.frame(enrichGO(gene = pnOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO6.df <- as.data.frame(enrichGO(gene = pnOver6, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO8.df <- as.data.frame(enrichGO(gene = pnOver8, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
#
#
# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver0\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver4\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset6 <- GO6.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver6\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset8 <- GO8.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver8\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
#
# GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
# \GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
# \GO:0072001\, \GO:0007517\, \GO:0048705\))
#
# data <- bind_rows(bind_rows(bind_rows(subset0, subset2), subset4), subset8) %>%
# dplyr::filter(ID %in% GOlist)
#
# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups_pn\)
# height = 2
# width = 3.3
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
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-->\n\n```r\n```r\n# GOfigDir <- here(figDir, \\../GO\\)\n# getGO(\\pnOver8\\, GOfigDir, pnOver8)\n# getGO(\\pnOver6\\, GOfigDir, pnOver6)\n# getGO(\\pnOver4\\, GOfigDir, pnOver4)\n# getGO(\\pnOver2\\, GOfigDir, pnOver2)\n# getGO(\\pnOver0\\, GOfigDir, pnOver0)\n# \n# #####################\n# GO0.df <- as.data.frame(enrichGO(gene = pnOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO2.df <- as.data.frame(enrichGO(gene = pnOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO4.df <- as.data.frame(enrichGO(gene = pnOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO6.df <- as.data.frame(enrichGO(gene = pnOver6, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO8.df <- as.data.frame(enrichGO(gene = pnOver8, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# \n# \n# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver0\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver4\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset6 <- GO6.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver6\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset8 <- GO8.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\pnOver8\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# \n# GOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n#                    \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n#                    \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\))\n# \n# data <- bind_rows(bind_rows(bind_rows(subset0, subset2), subset4), subset8) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pn\\)\n# height = 2\n# width = 3.3\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
# GOfigDir <- here(figDir, \../GO\)
# getGO(\pnOver8\, GOfigDir, pnOver8)
# getGO(\pnOver6\, GOfigDir, pnOver6)
# getGO(\pnOver4\, GOfigDir, pnOver4)
# getGO(\pnOver2\, GOfigDir, pnOver2)
# getGO(\pnOver0\, GOfigDir, pnOver0)
#
# #####################
# GO0.df <- as.data.frame(enrichGO(gene = pnOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO2.df <- as.data.frame(enrichGO(gene = pnOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO4.df <- as.data.frame(enrichGO(gene = pnOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO6.df <- as.data.frame(enrichGO(gene = pnOver6, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO8.df <- as.data.frame(enrichGO(gene = pnOver8, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
#
#
# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver0\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver4\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset6 <- GO6.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver6\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset8 <- GO8.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \pnOver8\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
#
# GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
# \GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
# \GO:0072001\, \GO:0007517\, \GO:0048705\))
#
# data <- bind_rows(bind_rows(bind_rows(subset0, subset2), subset4), subset8) %>%
# dplyr::filter(ID %in% GOlist)
#
# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups_pn\)
# height = 2
# width = 3.3
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### A485
#### Grouping with P-N number
##### Grouping
<!-- rnb-text-end -->
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# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$mean_diff_score
# distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$mean_diff_score
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# pv02 <- round(getPvalWilcox(temp, \pnOver0\, \pnOver2\), 5)
# pv24 <- round(getPvalWilcox(temp, \pnOver2\, \pnOver4\), 5)
# pv46 <- round(getPvalWilcox(temp, \pnOver4\, \pnOver6\), 5)
# pv08 <- round(getPvalWilcox(temp, \pnOver0\, \pnOver8\), 5)
# pv28 <- round(getPvalWilcox(temp, \pnOver2\, \pnOver8\), 5)
# pv48 <- round(getPvalWilcox(temp, \pnOver4\, \pnOver8\), 5)
# pv68 <- round(getPvalWilcox(temp, \pnOver6\, \pnOver8\), 5)
p <- ggplot(temp, aes(x = pnOver, y = mean_diff_score)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
# \pv24: \, convPvalue(pv24), \\n\,
# \pv46: \, convPvalue(pv46), \\n\,
# \pv68: \, convPvalue(pv68), \\n\,
# \pv48: \, convPvalue(pv48), \\n\,
# \pv28: \, convPvalue(pv28), \\n\,
# \pv08: \, convPvalue(pv08), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_pnGroup_A485_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->
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```r
```r
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$mean_diff_score
# distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$mean_diff_score
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# pv02 <- round(getPvalWilcox(temp, \pnOver0\, \pnOver2\), 5)
# pv24 <- round(getPvalWilcox(temp, \pnOver2\, \pnOver4\), 5)
# pv46 <- round(getPvalWilcox(temp, \pnOver4\, \pnOver6\), 5)
# pv08 <- round(getPvalWilcox(temp, \pnOver0\, \pnOver8\), 5)
# pv28 <- round(getPvalWilcox(temp, \pnOver2\, \pnOver8\), 5)
# pv48 <- round(getPvalWilcox(temp, \pnOver4\, \pnOver8\), 5)
# pv68 <- round(getPvalWilcox(temp, \pnOver6\, \pnOver8\), 5)
p <- ggplot(temp, aes(x = pnOver, y = mean_diff_score)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
# \pv24: \, convPvalue(pv24), \\n\,
# \pv46: \, convPvalue(pv46), \\n\,
# \pv68: \, convPvalue(pv68), \\n\,
# \pv48: \, convPvalue(pv48), \\n\,
# \pv28: \, convPvalue(pv28), \\n\,
# \pv08: \, convPvalue(pv08), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_pnGroup_A485_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
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<!-- rnb-text-begin -->
##### (SKIP) GO for each group
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n#\n#  getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$log2FoldChange\n#   distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$log2FoldChange\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# \ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n#  \n# pv02 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver2\\), 5)\n# pv04 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver4\\), 5)\n# pv06 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver6\\), 5)\n# pv08 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver8\\), 5)\n# pv24 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver4\\), 5)\n# pv26 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver6\\), 5)\n# pv28 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver8\\), 5)\n# pv46 <- round(getPvalWilcox(tempDown, \\pnOver4\\, \\pnOver6\\), 5)\n# pv48 <- round(getPvalWilcox(tempDown, \\pnOver4\\, \\pnOver8\\), 5)\n# pv68 <- round(getPvalWilcox(tempDown, \\pnOver6\\, \\pnOver8\\), 5)\n\n\n\n\np <- ggplot(tempDown, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = pnOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\pv02: \\, convPvalue(pv02), \\\\n\\,\n  #                                                  \\pv04: \\, convPvalue(pv04), \\\\n\\,\n  #                                                  \\pv06: \\, convPvalue(pv06), \\\\n\\,\n  #                                                  \\pv08: \\, convPvalue(pv08), \\\\n\\,\n  #                                                  \\pv24: \\, convPvalue(pv24), \\\\n\\,\n  #                                                  \\pv26: \\, convPvalue(pv26), \\\\n\\,\n  #                                                  \\pv28: \\, convPvalue(pv28), \\\\n\\,\n  #                                                  \\pv46: \\, convPvalue(pv46), \\\\n\\,\n  #                                                  \\pv48: \\, convPvalue(pv48), \\\\n\\,\n  #                                                  \\pv68: \\, convPvalue(pv68), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_pnGroup_A485_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n#  \n# pv02 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver2\\), 5)\n# pv04 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver4\\), 5)\n# pv06 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver6\\), 5)\n# pv08 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver8\\), 5)\n# pv24 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver4\\), 5)\n# pv26 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver6\\), 5)\n# pv28 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver8\\), 5)\n# pv46 <- round(getPvalWilcox(tempUp, \\pnOver4\\, \\pnOver6\\), 5)\n# pv48 <- round(getPvalWilcox(tempUp, \\pnOver4\\, \\pnOver8\\), 5)\n# pv68 <- round(getPvalWilcox(tempUp, \\pnOver6\\, \\pnOver8\\), 5)\n# \n\n\n\np <- ggplot(tempUp, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = pnOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\pv02: \\, convPvalue(pv02), \\\\n\\,\n  #                                                  \\pv04: \\, convPvalue(pv04), \\\\n\\,\n  #                                                  \\pv06: \\, convPvalue(pv06), \\\\n\\,\n  #                                                  \\pv08: \\, convPvalue(pv08), \\\\n\\,\n  #                                                  \\pv24: \\, convPvalue(pv24), \\\\n\\,\n  #                                                  \\pv26: \\, convPvalue(pv26), \\\\n\\,\n  #                                                  \\pv28: \\, convPvalue(pv28), \\\\n\\,\n  #                                                  \\pv46: \\, convPvalue(pv46), \\\\n\\,\n  #                                                  \\pv48: \\, convPvalue(pv48), \\\\n\\,\n  #                                                  \\pv68: \\, convPvalue(pv68), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_pnGroup_A485_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$log2FoldChange
# distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$log2FoldChange
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
#
# pv02 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver2\), 5)
# pv04 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver4\), 5)
# pv06 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver6\), 5)
# pv08 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver8\), 5)
# pv24 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver4\), 5)
# pv26 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver6\), 5)
# pv28 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver8\), 5)
# pv46 <- round(getPvalWilcox(tempDown, \pnOver4\, \pnOver6\), 5)
# pv48 <- round(getPvalWilcox(tempDown, \pnOver4\, \pnOver8\), 5)
# pv68 <- round(getPvalWilcox(tempDown, \pnOver6\, \pnOver8\), 5)
p <- ggplot(tempDown, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = -0.5, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
# \pv04: \, convPvalue(pv04), \\n\,
# \pv06: \, convPvalue(pv06), \\n\,
# \pv08: \, convPvalue(pv08), \\n\,
# \pv24: \, convPvalue(pv24), \\n\,
# \pv26: \, convPvalue(pv26), \\n\,
# \pv28: \, convPvalue(pv28), \\n\,
# \pv46: \, convPvalue(pv46), \\n\,
# \pv48: \, convPvalue(pv48), \\n\,
# \pv68: \, convPvalue(pv68), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_pnGroup_A485_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
#
# pv02 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver2\), 5)
# pv04 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver4\), 5)
# pv06 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver6\), 5)
# pv08 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver8\), 5)
# pv24 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver4\), 5)
# pv26 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver6\), 5)
# pv28 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver8\), 5)
# pv46 <- round(getPvalWilcox(tempUp, \pnOver4\, \pnOver6\), 5)
# pv48 <- round(getPvalWilcox(tempUp, \pnOver4\, \pnOver8\), 5)
# pv68 <- round(getPvalWilcox(tempUp, \pnOver6\, \pnOver8\), 5)
#
p <- ggplot(tempUp, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0.5, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
# \pv04: \, convPvalue(pv04), \\n\,
# \pv06: \, convPvalue(pv06), \\n\,
# \pv08: \, convPvalue(pv08), \\n\,
# \pv24: \, convPvalue(pv24), \\n\,
# \pv26: \, convPvalue(pv26), \\n\,
# \pv28: \, convPvalue(pv28), \\n\,
# \pv46: \, convPvalue(pv46), \\n\,
# \pv48: \, convPvalue(pv48), \\n\,
# \pv68: \, convPvalue(pv68), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_pnGroup_A485_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n#\n#  getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$log2FoldChange\n#   distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$log2FoldChange\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# \ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n#  \n# pv02 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver2\\), 5)\n# pv04 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver4\\), 5)\n# pv06 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver6\\), 5)\n# pv08 <- round(getPvalWilcox(tempDown, \\pnOver0\\, \\pnOver8\\), 5)\n# pv24 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver4\\), 5)\n# pv26 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver6\\), 5)\n# pv28 <- round(getPvalWilcox(tempDown, \\pnOver2\\, \\pnOver8\\), 5)\n# pv46 <- round(getPvalWilcox(tempDown, \\pnOver4\\, \\pnOver6\\), 5)\n# pv48 <- round(getPvalWilcox(tempDown, \\pnOver4\\, \\pnOver8\\), 5)\n# pv68 <- round(getPvalWilcox(tempDown, \\pnOver6\\, \\pnOver8\\), 5)\n\n\n\n\np <- ggplot(tempDown, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = pnOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\pv02: \\, convPvalue(pv02), \\\\n\\,\n  #                                                  \\pv04: \\, convPvalue(pv04), \\\\n\\,\n  #                                                  \\pv06: \\, convPvalue(pv06), \\\\n\\,\n  #                                                  \\pv08: \\, convPvalue(pv08), \\\\n\\,\n  #                                                  \\pv24: \\, convPvalue(pv24), \\\\n\\,\n  #                                                  \\pv26: \\, convPvalue(pv26), \\\\n\\,\n  #                                                  \\pv28: \\, convPvalue(pv28), \\\\n\\,\n  #                                                  \\pv46: \\, convPvalue(pv46), \\\\n\\,\n  #                                                  \\pv48: \\, convPvalue(pv48), \\\\n\\,\n  #                                                  \\pv68: \\, convPvalue(pv68), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_pnGroup_A485_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n#  \n# pv02 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver2\\), 5)\n# pv04 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver4\\), 5)\n# pv06 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver6\\), 5)\n# pv08 <- round(getPvalWilcox(tempUp, \\pnOver0\\, \\pnOver8\\), 5)\n# pv24 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver4\\), 5)\n# pv26 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver6\\), 5)\n# pv28 <- round(getPvalWilcox(tempUp, \\pnOver2\\, \\pnOver8\\), 5)\n# pv46 <- round(getPvalWilcox(tempUp, \\pnOver4\\, \\pnOver6\\), 5)\n# pv48 <- round(getPvalWilcox(tempUp, \\pnOver4\\, \\pnOver8\\), 5)\n# pv68 <- round(getPvalWilcox(tempUp, \\pnOver6\\, \\pnOver8\\), 5)\n# \n\n\n\np <- ggplot(tempUp, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = pnOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\pv02: \\, convPvalue(pv02), \\\\n\\,\n  #                                                  \\pv04: \\, convPvalue(pv04), \\\\n\\,\n  #                                                  \\pv06: \\, convPvalue(pv06), \\\\n\\,\n  #                                                  \\pv08: \\, convPvalue(pv08), \\\\n\\,\n  #                                                  \\pv24: \\, convPvalue(pv24), \\\\n\\,\n  #                                                  \\pv26: \\, convPvalue(pv26), \\\\n\\,\n  #                                                  \\pv28: \\, convPvalue(pv28), \\\\n\\,\n  #                                                  \\pv46: \\, convPvalue(pv46), \\\\n\\,\n  #                                                  \\pv48: \\, convPvalue(pv48), \\\\n\\,\n  #                                                  \\pv68: \\, convPvalue(pv68), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_pnGroup_A485_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$log2FoldChange
# distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$log2FoldChange
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
#
# pv02 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver2\), 5)
# pv04 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver4\), 5)
# pv06 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver6\), 5)
# pv08 <- round(getPvalWilcox(tempDown, \pnOver0\, \pnOver8\), 5)
# pv24 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver4\), 5)
# pv26 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver6\), 5)
# pv28 <- round(getPvalWilcox(tempDown, \pnOver2\, \pnOver8\), 5)
# pv46 <- round(getPvalWilcox(tempDown, \pnOver4\, \pnOver6\), 5)
# pv48 <- round(getPvalWilcox(tempDown, \pnOver4\, \pnOver8\), 5)
# pv68 <- round(getPvalWilcox(tempDown, \pnOver6\, \pnOver8\), 5)
p <- ggplot(tempDown, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = -0.5, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
# \pv04: \, convPvalue(pv04), \\n\,
# \pv06: \, convPvalue(pv06), \\n\,
# \pv08: \, convPvalue(pv08), \\n\,
# \pv24: \, convPvalue(pv24), \\n\,
# \pv26: \, convPvalue(pv26), \\n\,
# \pv28: \, convPvalue(pv28), \\n\,
# \pv46: \, convPvalue(pv46), \\n\,
# \pv48: \, convPvalue(pv48), \\n\,
# \pv68: \, convPvalue(pv68), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_pnGroup_A485_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
#
# pv02 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver2\), 5)
# pv04 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver4\), 5)
# pv06 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver6\), 5)
# pv08 <- round(getPvalWilcox(tempUp, \pnOver0\, \pnOver8\), 5)
# pv24 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver4\), 5)
# pv26 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver6\), 5)
# pv28 <- round(getPvalWilcox(tempUp, \pnOver2\, \pnOver8\), 5)
# pv46 <- round(getPvalWilcox(tempUp, \pnOver4\, \pnOver6\), 5)
# pv48 <- round(getPvalWilcox(tempUp, \pnOver4\, \pnOver8\), 5)
# pv68 <- round(getPvalWilcox(tempUp, \pnOver6\, \pnOver8\), 5)
#
p <- ggplot(tempUp, aes(x = pnOver, y = log2FoldChange)) + geom_violin(aes(fill = pnOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = pnOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0.5, label = paste0(\pv02: \, convPvalue(pv02), \\n\,
# \pv04: \, convPvalue(pv04), \\n\,
# \pv06: \, convPvalue(pv06), \\n\,
# \pv08: \, convPvalue(pv08), \\n\,
# \pv24: \, convPvalue(pv24), \\n\,
# \pv26: \, convPvalue(pv26), \\n\,
# \pv28: \, convPvalue(pv28), \\n\,
# \pv46: \, convPvalue(pv46), \\n\,
# \pv48: \, convPvalue(pv48), \\n\,
# \pv68: \, convPvalue(pv68), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_pnGroup_A485_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### loop score
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
psOver = ifelse(gene %in% psOver4, \psOver4\,
ifelse(gene %in% psOver3, \psOver3\,
ifelse(gene %in% psOver2, \psOver2\,
ifelse(gene %in% psOver1, \psOver1\,
ifelse(gene %in% psOver0, \psOver0\, NA)))))) %>%
drop_na(psOver)
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<!-- rnb-source-begin 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 -->
```r
```r
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
psOver = ifelse(gene %in% psOver4, \psOver4\,
ifelse(gene %in% psOver3, \psOver3\,
ifelse(gene %in% psOver2, \psOver2\,
ifelse(gene %in% psOver1, \psOver1\,
ifelse(gene %in% psOver0, \psOver0\, NA)))))) %>%
drop_na(psOver)
```
```
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##### log2FC
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eHQoc2l6ZSA9IDYpLCAjIFNldCBheGlzIHRpdGxlIHNpemUgKGlmIG5vdCByZW1vdmVkKVxuIyAgICAgICAgIGxlZ2VuZC50ZXh0ID0gZWxlbWVudF90ZXh0KHNpemUgPSA2KSwgIyBTZXQgbGVnZW5kIHRleHQgc2l6ZVxuIyAgICAgICAgIGxlZ2VuZC50aXRsZSA9IGVsZW1lbnRfdGV4dChzaXplID0gNikpIFxuIyBcbiMgZmlsZU5hbWUgPC0gaGVyZShmaWdEaXIsIFwiLi5cIiwgXCJHT1wiLCBcIkdPX2dyb3Vwc19wc1wiKVxuIyBoZWlnaHQgPSAyXG4jIHdpZHRoID0gMy40XG4jIHN2Z2xpdGUocGFzdGUwKGZpbGVOYW1lLCBcIi5zdmdcIiksIGhlaWdodCA9IGhlaWdodCwgd2lkdGggPSB3aWR0aClcbiMgcHJpbnQocClcbiMgZGV2Lm9mZigpXG5gYGAifQ== -->\n\n```r\n# GOfigDir <- here(figDir, \\../GO\\)\n# getGO(\\psOver4\\, GOfigDir, psOver4)\n# getGO(\\psOver3\\, GOfigDir, psOver3)\n# getGO(\\psOver2\\, GOfigDir, psOver2)\n# getGO(\\psOver1\\, GOfigDir, psOver1)\n# getGO(\\psOver0\\, GOfigDir, psOver0)\n# \n# #####################\n# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# \n# \n# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver0\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver1\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver3\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver4\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# \n# GOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n#                    \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n#                    \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\, \\GO:0003002\\))\n# \n# data <- bind_rows(bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset3), subset4) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_ps\\)\n# height = 2\n# width = 3.4\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# GOfigDir <- here(figDir, \../GO\)
# getGO(\psOver4\, GOfigDir, psOver4)
# getGO(\psOver3\, GOfigDir, psOver3)
# getGO(\psOver2\, GOfigDir, psOver2)
# getGO(\psOver1\, GOfigDir, psOver1)
# getGO(\psOver0\, GOfigDir, psOver0)
#
# #####################
# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
#
#
# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver0\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver1\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver3\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver4\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
#
# GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
# \GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
# \GO:0072001\, \GO:0007517\, \GO:0048705\, \GO:0003002\))
#
# data <- bind_rows(bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset3), subset4) %>%
# dplyr::filter(ID %in% GOlist)
#
# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups_ps\)
# height = 2
# width = 3.4
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
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-->\n\n```r\n```r\n# GOfigDir <- here(figDir, \\../GO\\)\n# getGO(\\psOver4\\, GOfigDir, psOver4)\n# getGO(\\psOver3\\, GOfigDir, psOver3)\n# getGO(\\psOver2\\, GOfigDir, psOver2)\n# getGO(\\psOver1\\, GOfigDir, psOver1)\n# getGO(\\psOver0\\, GOfigDir, psOver0)\n# \n# #####################\n# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# \n# \n# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver0\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver1\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver3\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver4\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# \n# GOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n#                    \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n#                    \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\, \\GO:0003002\\))\n# \n# data <- bind_rows(bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset3), subset4) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_ps\\)\n# height = 2\n# width = 3.4\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
# GOfigDir <- here(figDir, \../GO\)
# getGO(\psOver4\, GOfigDir, psOver4)
# getGO(\psOver3\, GOfigDir, psOver3)
# getGO(\psOver2\, GOfigDir, psOver2)
# getGO(\psOver1\, GOfigDir, psOver1)
# getGO(\psOver0\, GOfigDir, psOver0)
#
# #####################
# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
#
#
# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver0\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver1\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver3\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver4\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
#
# GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
# \GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
# \GO:0072001\, \GO:0007517\, \GO:0048705\, \GO:0003002\))
#
# data <- bind_rows(bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset3), subset4) %>%
# dplyr::filter(ID %in% GOlist)
#
# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups_ps\)
# height = 2
# width = 3.4
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Grouping with P-S number
##### Grouping
<!-- rnb-text-end -->
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# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
#
p <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = psOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_psGroup_A485_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->
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```r
```r
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
#
p <- ggplot(temp, aes(x = psOver, y = mean_diff_score)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = psOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_psGroup_A485_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-text-begin -->
##### (SKIP) GO for each group
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-->\n\n```r\n#\n#  getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n#   distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n#  \n# ps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n# \n# ps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = psOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_A485_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n#  \n# ps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n# \n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = psOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_A485_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
#
# ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
#
# ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = psOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_psGroup_A485_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
#
# ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
#
p <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = psOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_psGroup_A485_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\n#\n#  getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n#   distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n#  \n# ps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n# \n# ps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = psOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_A485_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n#  \n# ps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n# \n\n\n\np <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = psOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_psGroup_A485_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
#
# ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
#
# ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = psOver, y = log2FoldChange)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = psOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_psGroup_A485_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
#
# ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
#
p <- ggplot(tempUp, aes(x = psOver, y = log2FoldChange)) + geom_violin(aes(fill = psOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = psOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_psGroup_A485_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### loop score
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# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
peOver = ifelse(gene %in% peOver4, \peOver4\,
ifelse(gene %in% peOver3, \peOver3\,
ifelse(gene %in% peOver2, \peOver2\,
ifelse(gene %in% peOver1, \peOver1\,
ifelse(gene %in% peOver0, \peOver0\, NA)))))) %>%
drop_na(peOver)
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```r
```r
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
peOver = ifelse(gene %in% peOver4, \peOver4\,
ifelse(gene %in% peOver3, \peOver3\,
ifelse(gene %in% peOver2, \peOver2\,
ifelse(gene %in% peOver1, \peOver1\,
ifelse(gene %in% peOver0, \peOver0\, NA)))))) %>%
drop_na(peOver)
```
```
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##### log2FC
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-->\n\n```r\n# GOfigDir <- here(figDir, \\../GO\\)\n# getGO(\\peOver4\\, GOfigDir, psOver4)\n# getGO(\\peOver3\\, GOfigDir, psOver3)\n# getGO(\\peOver2\\, GOfigDir, psOver2)\n# getGO(\\peOver1\\, GOfigDir, psOver1)\n# getGO(\\peOver0\\, GOfigDir, psOver0)\n# \n# #####################\n# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# \n# \n# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver0\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver1\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver3\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver4\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# \n# GOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n#                    \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n#                    \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\, \n#                    \\GO:0040029\\, \\GO:0010165\\))\n# \n# data <- bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset4) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pe\\)\n# height = 2\n# width = 3.4\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# GOfigDir <- here(figDir, \../GO\)
# getGO(\peOver4\, GOfigDir, psOver4)
# getGO(\peOver3\, GOfigDir, psOver3)
# getGO(\peOver2\, GOfigDir, psOver2)
# getGO(\peOver1\, GOfigDir, psOver1)
# getGO(\peOver0\, GOfigDir, psOver0)
#
# #####################
# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
#
#
# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver0\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver1\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver3\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver4\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
#
# GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
# \GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
# \GO:0072001\, \GO:0007517\, \GO:0048705\,
# \GO:0040029\, \GO:0010165\))
#
# data <- bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset4) %>%
# dplyr::filter(ID %in% GOlist)
#
# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups_pe\)
# height = 2
# width = 3.4
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
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-->\n\n```r\n```r\n# GOfigDir <- here(figDir, \\../GO\\)\n# getGO(\\peOver4\\, GOfigDir, psOver4)\n# getGO(\\peOver3\\, GOfigDir, psOver3)\n# getGO(\\peOver2\\, GOfigDir, psOver2)\n# getGO(\\peOver1\\, GOfigDir, psOver1)\n# getGO(\\peOver0\\, GOfigDir, psOver0)\n# \n# #####################\n# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# \n# \n# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver0\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver1\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver3\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver4\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# \n# GOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n#                    \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n#                    \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\, \n#                    \\GO:0040029\\, \\GO:0010165\\))\n# \n# data <- bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset4) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pe\\)\n# height = 2\n# width = 3.4\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
# GOfigDir <- here(figDir, \../GO\)
# getGO(\peOver4\, GOfigDir, psOver4)
# getGO(\peOver3\, GOfigDir, psOver3)
# getGO(\peOver2\, GOfigDir, psOver2)
# getGO(\peOver1\, GOfigDir, psOver1)
# getGO(\peOver0\, GOfigDir, psOver0)
#
# #####################
# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
#
#
# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver0\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver1\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver3\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver4\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
#
# GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
# \GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
# \GO:0072001\, \GO:0007517\, \GO:0048705\,
# \GO:0040029\, \GO:0010165\))
#
# data <- bind_rows(bind_rows(bind_rows(subset0, subset1), subset2), subset4) %>%
# dplyr::filter(ID %in% GOlist)
#
# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups_pe\)
# height = 2
# width = 3.4
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Grouping with P-E number
##### Grouping
<!-- rnb-text-end -->
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<!-- rnb-output-begin 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-->
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = peOver, y = mean_diff_score)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = peOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \,convPvalue( ps14), \\n\,
# \ps04: \,convPvalue( ps04), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_peGroup_A485_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->
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```r
```r
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = peOver, y = mean_diff_score)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = peOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \,convPvalue( ps14), \\n\,
# \ps04: \,convPvalue( ps04), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_peGroup_A485_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### (SKIP) GO for each group
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-->\n\n```r\n#\n#  getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n#   distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# \ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n#  \n# ps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = peOver, y = log2FoldChange)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = peOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_A485_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \n# ps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n# \n\n\n\n\np <- ggplot(tempUp, aes(x = peOver, y = log2FoldChange)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = peOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_A485_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
#
# ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = peOver, y = log2FoldChange)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = peOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_peGroup_A485_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
# ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
#
p <- ggplot(tempUp, aes(x = peOver, y = log2FoldChange)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = peOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_peGroup_A485_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\n#\n#  getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n#   distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# \ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n#  \n# ps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\np <- ggplot(tempDown, aes(x = peOver, y = log2FoldChange)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = peOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_A485_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n \n# ps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n# \n\n\n\n\np <- ggplot(tempUp, aes(x = peOver, y = log2FoldChange)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = peOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_peGroup_A485_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
#
# ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = peOver, y = log2FoldChange)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = peOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_peGroup_A485_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
# ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
#
p <- ggplot(tempUp, aes(x = peOver, y = log2FoldChange)) + geom_violin(aes(fill = peOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = peOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_peGroup_A485_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### loop score
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# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
ppOver = ifelse(gene %in% ppOver4, \ppOver4\,
ifelse(gene %in% ppOver3, \ppOver3\,
ifelse(gene %in% ppOver2, \ppOver2\,
ifelse(gene %in% ppOver1, \ppOver1\,
ifelse(gene %in% ppOver0, \ppOver0\, NA)))))) %>%
drop_na(ppOver)
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```r
```r
# temp2 contains genes from group 1, 2, 5, 8 and loop counts
#calculating diff score and log2fc distribution based on p-n numbers
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_A485_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_A485_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
ppOver = ifelse(gene %in% ppOver4, \ppOver4\,
ifelse(gene %in% ppOver3, \ppOver3\,
ifelse(gene %in% ppOver2, \ppOver2\,
ifelse(gene %in% ppOver1, \ppOver1\,
ifelse(gene %in% ppOver0, \ppOver0\, NA)))))) %>%
drop_na(ppOver)
```
```
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##### log2FC
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IHRpdGxlIHNpemUgKGlmIG5vdCByZW1vdmVkKVxuIyAgICAgICAgIGxlZ2VuZC50ZXh0ID0gZWxlbWVudF90ZXh0KHNpemUgPSA2KSwgIyBTZXQgbGVnZW5kIHRleHQgc2l6ZVxuIyAgICAgICAgIGxlZ2VuZC50aXRsZSA9IGVsZW1lbnRfdGV4dChzaXplID0gNikpIFxuIyBcbiMgZmlsZU5hbWUgPC0gaGVyZShmaWdEaXIsIFwiLi5cIiwgXCJHT1wiLCBcIkdPX2dyb3Vwc19wcFwiKVxuIyBoZWlnaHQgPSAyXG4jIHdpZHRoID0gMy40XG4jIHN2Z2xpdGUocGFzdGUwKGZpbGVOYW1lLCBcIi5zdmdcIiksIGhlaWdodCA9IGhlaWdodCwgd2lkdGggPSB3aWR0aClcbiMgcHJpbnQocClcbiMgZGV2Lm9mZigpXG5gYGAifQ== -->\n\n```r\n# GOfigDir <- here(figDir, \\../GO\\)\n# getGO(\\ppOver4\\, GOfigDir, psOver4)\n# getGO(\\ppOver3\\, GOfigDir, psOver3)\n# getGO(\\ppOver2\\, GOfigDir, psOver2)\n# getGO(\\ppOver1\\, GOfigDir, psOver1)\n# getGO(\\ppOver0\\, GOfigDir, psOver0)\n# \n# #####################\n# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# \n# \n# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver0\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver1\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver3\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver4\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# \n# GOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n#                    \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n#                    \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\, \\GO:0003002\\, \\GO:0009411\\))\n# \n# data <- bind_rows(bind_rows(subset0, subset1), subset3) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pp\\)\n# height = 2\n# width = 3.4\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# GOfigDir <- here(figDir, \../GO\)
# getGO(\ppOver4\, GOfigDir, psOver4)
# getGO(\ppOver3\, GOfigDir, psOver3)
# getGO(\ppOver2\, GOfigDir, psOver2)
# getGO(\ppOver1\, GOfigDir, psOver1)
# getGO(\ppOver0\, GOfigDir, psOver0)
#
# #####################
# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
#
#
# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver0\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver1\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver3\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver4\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
#
# GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
# \GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
# \GO:0072001\, \GO:0007517\, \GO:0048705\, \GO:0003002\, \GO:0009411\))
#
# data <- bind_rows(bind_rows(subset0, subset1), subset3) %>%
# dplyr::filter(ID %in% GOlist)
#
# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups_pp\)
# height = 2
# width = 3.4
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
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-->\n\n```r\n```r\n# GOfigDir <- here(figDir, \\../GO\\)\n# getGO(\\ppOver4\\, GOfigDir, psOver4)\n# getGO(\\ppOver3\\, GOfigDir, psOver3)\n# getGO(\\ppOver2\\, GOfigDir, psOver2)\n# getGO(\\ppOver1\\, GOfigDir, psOver1)\n# getGO(\\ppOver0\\, GOfigDir, psOver0)\n# \n# #####################\n# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n# \n# \n# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver0\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver1\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver2\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver3\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\psOver4\\) %>%\n#   dplyr::mutate(\n#     gr = sapply(GeneRatio, function(x) {\n#       # Split the string by \\/\\\n#       parts <- unlist(strsplit(x, \\/\\))\n#       # Convert to numeric and perform the division\n#       as.numeric(parts[1]) / as.numeric(parts[2])\n#     })\n#   ) %>% dplyr::arrange(desc(gr))\n# \n# \n# GOlist <- factor(c(\\GO:0006397\\, \\GO:0008380\\, \\GO:0022613\\, \\GO:0034470\\,\n#                    \\GO:0016055\\, \\GO:0007389\\, \\GO:0048562\\, \\GO:0045165\\, \n#                    \\GO:0072001\\, \\GO:0007517\\, \\GO:0048705\\, \\GO:0003002\\, \\GO:0009411\\))\n# \n# data <- bind_rows(bind_rows(subset0, subset1), subset3) %>%\n#   dplyr::filter(ID %in% GOlist)\n# \n# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n#   geom_point() + theme_bw() +\n#   scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n#   scale_size_continuous(range = c(0, 3)) +\n#   labs(x = NULL, y = NULL) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- here(figDir, \\..\\, \\GO\\, \\GO_groups_pp\\)\n# height = 2\n# width = 3.4\n# svglite(paste0(fileName, \\.svg\\), height = height, width = width)\n# print(p)\n# dev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
# GOfigDir <- here(figDir, \../GO\)
# getGO(\ppOver4\, GOfigDir, psOver4)
# getGO(\ppOver3\, GOfigDir, psOver3)
# getGO(\ppOver2\, GOfigDir, psOver2)
# getGO(\ppOver1\, GOfigDir, psOver1)
# getGO(\ppOver0\, GOfigDir, psOver0)
#
# #####################
# GO0.df <- as.data.frame(enrichGO(gene = psOver0, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO1.df <- as.data.frame(enrichGO(gene = psOver1, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO2.df <- as.data.frame(enrichGO(gene = psOver2, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO3.df <- as.data.frame(enrichGO(gene = psOver3, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
# GO4.df <- as.data.frame(enrichGO(gene = psOver4, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
#
#
# subset0 <- GO0.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver0\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset1 <- GO1.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver1\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset2 <- GO2.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver2\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset3 <- GO3.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver3\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
# subset4 <- GO4.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \psOver4\) %>%
# dplyr::mutate(
# gr = sapply(GeneRatio, function(x) {
# # Split the string by \/\
# parts <- unlist(strsplit(x, \/\))
# # Convert to numeric and perform the division
# as.numeric(parts[1]) / as.numeric(parts[2])
# })
# ) %>% dplyr::arrange(desc(gr))
#
#
# GOlist <- factor(c(\GO:0006397\, \GO:0008380\, \GO:0022613\, \GO:0034470\,
# \GO:0016055\, \GO:0007389\, \GO:0048562\, \GO:0045165\,
# \GO:0072001\, \GO:0007517\, \GO:0048705\, \GO:0003002\, \GO:0009411\))
#
# data <- bind_rows(bind_rows(subset0, subset1), subset3) %>%
# dplyr::filter(ID %in% GOlist)
#
# p <- ggplot(data, aes(x = group, y = Description, color = p.adjust, size = gr)) +
# geom_point() + theme_bw() +
# scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
# scale_size_continuous(range = c(0, 3)) +
# labs(x = NULL, y = NULL) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- here(figDir, \..\, \GO\, \GO_groups_pp\)
# height = 2
# width = 3.4
# svglite(paste0(fileName, \.svg\), height = height, width = width)
# print(p)
# dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Grouping with P-P number
##### Grouping
<!-- rnb-text-end -->
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<!-- rnb-output-begin 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-->
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = ppOver, y = mean_diff_score)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = ppOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_ppGroup_A485_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->
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```r
```r
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$mean_diff_score
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$mean_diff_score
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
# ps01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(temp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(temp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
p <- ggplot(temp, aes(x = ppOver, y = mean_diff_score)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = ppOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\diffScore_barplot_ppGroup_A485_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-text-begin -->
##### (SKIP) GO for each group
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-->\n\n```r\n#\n#  getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n#   distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n#  \n# ps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\n\np <- ggplot(tempDown, aes(x = ppOver, y = log2FoldChange)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = ppOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_A485_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n#  \n# ps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n# \n# \n\n\n\np <- ggplot(tempUp, aes(x = ppOver, y = log2FoldChange)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = ppOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_A485_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
#
# ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = ppOver, y = log2FoldChange)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = ppOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_ppGroup_A485_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
#
# ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
#
#
p <- ggplot(tempUp, aes(x = ppOver, y = log2FoldChange)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = ppOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_ppGroup_A485_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\n#\n#  getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange\n#   distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n\ntempDown <- temp %>% dplyr::filter(log2FoldChange < 0)\n#  \n# ps01 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempDown, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempDown, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempDown, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempDown, \\psOver0\\, \\psOver4\\), 5)\n\n\n\np <- ggplot(tempDown, aes(x = ppOver, y = log2FoldChange)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = ppOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = -0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))\n\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_A485_vs_DMSO_down\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\ntempUp <- temp %>% dplyr::filter(log2FoldChange > 0)\n#  \n# ps01 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver1\\), 5)\n# ps12 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver2\\), 5)\n# ps23 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver3\\), 5)\n# ps34 <- round(getPvalWilcox(tempUp, \\psOver3\\, \\psOver4\\), 5)\n# ps24 <- round(getPvalWilcox(tempUp, \\psOver2\\, \\psOver4\\), 5)\n# ps14 <- round(getPvalWilcox(tempUp, \\psOver1\\, \\psOver4\\), 5)\n# ps04 <- round(getPvalWilcox(tempUp, \\psOver0\\, \\psOver4\\), 5)\n# \n# \n\n\n\np <- ggplot(tempUp, aes(x = ppOver, y = log2FoldChange)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +\n  geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() + \n  stat_summary(aes(group = ppOver), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  geom_hline(yintercept = 0)\n  # annotate(\\text\\, x = 1, y = 0.5, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n  #                                               \\ps12: \\, convPvalue(ps12), \\\\n\\,\n  #                                               \\ps23: \\, convPvalue(ps23), \\\\n\\,\n  #                                               \\ps34: \\, convPvalue(ps34), \\\\n\\,\n  #                                               \\ps24: \\, convPvalue(ps24), \\\\n\\,\n  #                                               \\ps14: \\, convPvalue(ps14), \\\\n\\,\n  #                                               \\ps04: \\, convPvalue(ps04), \\\\n\\), \n  #          color = \\black\\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))\n\n  \nfileName <- paste0(\\log2FC_barplot_ppGroup_A485_vs_DMSO_up\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
#
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(psOver ==group1) )$log2FoldChange
# distance2 <- (data %>% dplyr::filter(psOver ==group2) )$log2FoldChange
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
tempDown <- temp %>% dplyr::filter(log2FoldChange < 0)
#
# ps01 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempDown, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempDown, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempDown, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempDown, \psOver0\, \psOver4\), 5)
p <- ggplot(tempDown, aes(x = ppOver, y = log2FoldChange)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = ppOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = -0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(-1, 0))
fileName <- paste0(\log2FC_barplot_ppGroup_A485_vs_DMSO_down\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
tempUp <- temp %>% dplyr::filter(log2FoldChange > 0)
#
# ps01 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver1\), 5)
# ps12 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver2\), 5)
# ps23 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver3\), 5)
# ps34 <- round(getPvalWilcox(tempUp, \psOver3\, \psOver4\), 5)
# ps24 <- round(getPvalWilcox(tempUp, \psOver2\, \psOver4\), 5)
# ps14 <- round(getPvalWilcox(tempUp, \psOver1\, \psOver4\), 5)
# ps04 <- round(getPvalWilcox(tempUp, \psOver0\, \psOver4\), 5)
#
#
p <- ggplot(tempUp, aes(x = ppOver, y = log2FoldChange)) + geom_violin(aes(fill = ppOver), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) + theme_classic() +
stat_summary(aes(group = ppOver), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
geom_hline(yintercept = 0)
# annotate(\text\, x = 1, y = 0.5, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
# \ps12: \, convPvalue(ps12), \\n\,
# \ps23: \, convPvalue(ps23), \\n\,
# \ps34: \, convPvalue(ps34), \\n\,
# \ps24: \, convPvalue(ps24), \\n\,
# \ps14: \, convPvalue(ps14), \\n\,
# \ps04: \, convPvalue(ps04), \\n\),
# color = \black\, hjust = 0, size = 3) + coord_cartesian(ylim = c(0, 1))
fileName <- paste0(\log2FC_barplot_ppGroup_A485_vs_DMSO_up\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### loop score
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# IMPORTING GENE ANNO DATA FOR P-N LOOPS\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                 diffCutoff = 0.2,\n                                 annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27, 28))\n\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>%\n  dplyr::mutate(binSize = end1 - start1,\n                adj_start1 = ifelse(binSize == 5000, start1,\n                                    ifelse(binSize == 10000, start1,\n                                           start1 + 10000)),\n                adj_end1 = adj_start1 + 5000,\n                adj_start2 = ifelse(binSize == 5000, start2,\n                                    ifelse(binSize == 10000, start2,\n                                           start2 + 10000)),\n                adj_end2 = adj_start2 + 5000,\n                anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \\_\\),\n                anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \\_\\))\n\n# data <- fread(here(consensusDir, paste0(name, \\.tsv\\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>% \n#   dplyr::mutate(binSize = end1 - start1,\n#                 adj_start1 = ifelse(binSize == 5000, start1,\n#                                     ifelse(binSize == 10000, start1+5000,\n#                                            start1 + 10000)),\n#                 adj_end1 = adj_start1 + 5000,\n#                 adj_start2 = ifelse(binSize == 5000, start2,\n#                                     ifelse(binSize == 10000, start2 + 5000,\n#                                            start2 + 10000)),\n#                 adj_end2 = adj_start2 + 5000,\n#                 anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \\_\\),\n#                 anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \\_\\))\n\ndata.reg <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\, \\E-E\\, \\E-S\\, \\E-X\\))\n\n## Graph\n#install.packages(\\igraph\\)\nlibrary(igraph)\n\n# To be able to construct a graph, each anchor need to have same resolution.\n# Shink the size to 10 kb\n# If the anchor is 25kb, choose the middle 5kb bin\n# If the anchor is 10kb, choose the left 5kb bin\n\ngraph <- graph_from_data_frame(data.reg %>% dplyr::select(anchor1, anchor2))\ncomponents <- components(graph)\ndata.reg$hub <- components$membership[data.reg$anchor1]\n\ntemp.bedpe <- data.reg %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)  \n\nhubNum <- data.reg %>% group_by(hub) %>% summarise(count = n())\n\n# Hub size distribution\np <- ggplot(hubNum, aes(x = count)) + geom_histogram(binwidth  = 1) + theme_classic() + \n  scale_y_log10()\n\nsvglite(here(figDir, \\hub_size_distribution.svg\\), height = 2, width = 2)\nprint(p)\ndev.off()\n\ndata.reg <- data.reg %>% dplyr::left_join(hubNum, by = \\hub\\) \ndata.reg <- data.reg %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n# Exporting regulatory loops with specific hub size\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 3) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub3.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)  \n\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 5) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub5.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)\n\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 10) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub10.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)\n\n\n# Adding gene annotation to hubs\ngenePeakPair <- geneAnnoData %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \\_\\)) %>% dplyr::select(peakID, gene)\ndata.reg <- data.reg %>% dplyr::left_join(genePeakPair, by = \\peakID\\)\n\n# During unnesting, E-N loops without gene annotated are removed\ndata.reg <- data.reg %>% unnest(gene) %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n)\n\n\ntemp <- data.reg %>% dplyr::select(gene, hub, count, group) %>%\n  group_by(gene) %>% slice_max(count, with_ties = FALSE) %>%\n  dplyr::filter(group %in% c(\\group1\\, \\group2\\, \\group5\\, \\group8\\))\n\n\n# TEMP START\n# TEMP START\ngeneList.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))\ngeneList.group1.temp <- geneList.group1 %>% dplyr::left_join(temp, by = c(\\gene\\))\n# TEMP END\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\npv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\npv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\npv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\npv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\npv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\npv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(temp, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() + \n    scale_y_continuous(breaks = seq(0, 30, by = 5), limits = c(0, 30)) + \n\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)  +\n  annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, pv12, \\\\n\\,\n                                                \\pv15: \\, pv15, \\\\n\\,\n                                                \\pv18: \\, pv18, \\\\n\\,\n                                                \\pv25: \\, pv25, \\\\n\\,\n                                                \\pv28: \\, pv28, \\\\n\\,\n                                                \\pv58: \\, pv58, \\\\n\\), \n           color = \\black\\, hjust = 0, size = 1) +  theme(legend.position = \\none\\)\n\n\nfileName <- paste0(\\countHub_barplot_diffGroup_dTAG_vs_DMSO\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# IMPORTING GENE ANNO DATA FOR P-N LOOPS
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\)) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27, 28))
data <- fread(here(consensusDir, paste0(name, \.tsv\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>%
dplyr::mutate(binSize = end1 - start1,
adj_start1 = ifelse(binSize == 5000, start1,
ifelse(binSize == 10000, start1,
start1 + 10000)),
adj_end1 = adj_start1 + 5000,
adj_start2 = ifelse(binSize == 5000, start2,
ifelse(binSize == 10000, start2,
start2 + 10000)),
adj_end2 = adj_start2 + 5000,
anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \_\),
anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \_\))
# data <- fread(here(consensusDir, paste0(name, \.tsv\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>%
# dplyr::mutate(binSize = end1 - start1,
# adj_start1 = ifelse(binSize == 5000, start1,
# ifelse(binSize == 10000, start1+5000,
# start1 + 10000)),
# adj_end1 = adj_start1 + 5000,
# adj_start2 = ifelse(binSize == 5000, start2,
# ifelse(binSize == 10000, start2 + 5000,
# start2 + 10000)),
# adj_end2 = adj_start2 + 5000,
# anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \_\),
# anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \_\))
data.reg <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \P-S\, \P-X\, \E-E\, \E-S\, \E-X\))
## Graph
#install.packages(\igraph\)
library(igraph)
# To be able to construct a graph, each anchor need to have same resolution.
# Shink the size to 10 kb
# If the anchor is 25kb, choose the middle 5kb bin
# If the anchor is 10kb, choose the left 5kb bin
graph <- graph_from_data_frame(data.reg %>% dplyr::select(anchor1, anchor2))
components <- components(graph)
data.reg$hub <- components$membership[data.reg$anchor1]
temp.bedpe <- data.reg %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)
fwrite(temp.bedpe, here(consensusDir, paste0(name, \_regulatory_5kbAdj.bedpe\)),
col.names = FALSE,row.names = FALSE, sep = \\t\)
hubNum <- data.reg %>% group_by(hub) %>% summarise(count = n())
# Hub size distribution
p <- ggplot(hubNum, aes(x = count)) + geom_histogram(binwidth = 1) + theme_classic() +
scale_y_log10()
svglite(here(figDir, \hub_size_distribution.svg\), height = 2, width = 2)
print(p)
dev.off()
data.reg <- data.reg %>% dplyr::left_join(hubNum, by = \hub\)
data.reg <- data.reg %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \_\))
# Exporting regulatory loops with specific hub size
temp.bedpe <- data.reg %>% dplyr::filter(count >= 3) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)
fwrite(temp.bedpe, here(consensusDir, paste0(name, \_regulatory_5kbAdj_hub3.bedpe\)),
col.names = FALSE,row.names = FALSE, sep = \\t\)
temp.bedpe <- data.reg %>% dplyr::filter(count >= 5) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)
fwrite(temp.bedpe, here(consensusDir, paste0(name, \_regulatory_5kbAdj_hub5.bedpe\)),
col.names = FALSE,row.names = FALSE, sep = \\t\)
temp.bedpe <- data.reg %>% dplyr::filter(count >= 10) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)
fwrite(temp.bedpe, here(consensusDir, paste0(name, \_regulatory_5kbAdj_hub10.bedpe\)),
col.names = FALSE,row.names = FALSE, sep = \\t\)
# Adding gene annotation to hubs
genePeakPair <- geneAnnoData %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \_\)) %>% dplyr::select(peakID, gene)
data.reg <- data.reg %>% dplyr::left_join(genePeakPair, by = \peakID\)
# During unnesting, E-N loops without gene annotated are removed
data.reg <- data.reg %>% unnest(gene) %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
)
temp <- data.reg %>% dplyr::select(gene, hub, count, group) %>%
group_by(gene) %>% slice_max(count, with_ties = FALSE) %>%
dplyr::filter(group %in% c(\group1\, \group2\, \group5\, \group8\))
# TEMP START
# TEMP START
geneList.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))
geneList.group1.temp <- geneList.group1 %>% dplyr::left_join(temp, by = c(\gene\))
# TEMP END
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$count
distance2 <- (data %>% dplyr::filter(group ==group2) )$count
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(tempSum,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(tempSum,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(tempSum,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(tempSum,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(tempSum,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(tempSum,\group5\, \group8\), 5)
p <- ggplot(temp, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 30, by = 5), limits = c(0, 30)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1) + theme(legend.position = \none\)
fileName <- paste0(\countHub_barplot_diffGroup_dTAG_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n# IMPORTING GENE ANNO DATA FOR P-N LOOPS\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                 diffCutoff = 0.2,\n                                 annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27, 28))\n\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>%\n  dplyr::mutate(binSize = end1 - start1,\n                adj_start1 = ifelse(binSize == 5000, start1,\n                                    ifelse(binSize == 10000, start1,\n                                           start1 + 10000)),\n                adj_end1 = adj_start1 + 5000,\n                adj_start2 = ifelse(binSize == 5000, start2,\n                                    ifelse(binSize == 10000, start2,\n                                           start2 + 10000)),\n                adj_end2 = adj_start2 + 5000,\n                anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \\_\\),\n                anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \\_\\))\n\n# data <- fread(here(consensusDir, paste0(name, \\.tsv\\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>% \n#   dplyr::mutate(binSize = end1 - start1,\n#                 adj_start1 = ifelse(binSize == 5000, start1,\n#                                     ifelse(binSize == 10000, start1+5000,\n#                                            start1 + 10000)),\n#                 adj_end1 = adj_start1 + 5000,\n#                 adj_start2 = ifelse(binSize == 5000, start2,\n#                                     ifelse(binSize == 10000, start2 + 5000,\n#                                            start2 + 10000)),\n#                 adj_end2 = adj_start2 + 5000,\n#                 anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \\_\\),\n#                 anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \\_\\))\n\ndata.reg <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\, \\E-E\\, \\E-S\\, \\E-X\\))\n\n## Graph\n#install.packages(\\igraph\\)\nlibrary(igraph)\n\n# To be able to construct a graph, each anchor need to have same resolution.\n# Shink the size to 10 kb\n# If the anchor is 25kb, choose the middle 5kb bin\n# If the anchor is 10kb, choose the left 5kb bin\n\ngraph <- graph_from_data_frame(data.reg %>% dplyr::select(anchor1, anchor2))\ncomponents <- components(graph)\ndata.reg$hub <- components$membership[data.reg$anchor1]\n\ntemp.bedpe <- data.reg %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)  \n\nhubNum <- data.reg %>% group_by(hub) %>% summarise(count = n())\n\n# Hub size distribution\np <- ggplot(hubNum, aes(x = count)) + geom_histogram(binwidth  = 1) + theme_classic() + \n  scale_y_log10()\n\nsvglite(here(figDir, \\hub_size_distribution.svg\\), height = 2, width = 2)\nprint(p)\ndev.off()\n\ndata.reg <- data.reg %>% dplyr::left_join(hubNum, by = \\hub\\) \ndata.reg <- data.reg %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n# Exporting regulatory loops with specific hub size\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 3) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub3.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)  \n\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 5) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub5.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)\n\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 10) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub10.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)\n\n\n# Adding gene annotation to hubs\ngenePeakPair <- geneAnnoData %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \\_\\)) %>% dplyr::select(peakID, gene)\ndata.reg <- data.reg %>% dplyr::left_join(genePeakPair, by = \\peakID\\)\n\n# During unnesting, E-N loops without gene annotated are removed\ndata.reg <- data.reg %>% unnest(gene) %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n)\n\n\ntemp <- data.reg %>% dplyr::select(gene, hub, count, group) %>%\n  group_by(gene) %>% slice_max(count, with_ties = FALSE) %>%\n  dplyr::filter(group %in% c(\\group1\\, \\group2\\, \\group5\\, \\group8\\))\n\n\n# TEMP START\n# TEMP START\ngeneList.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))\ngeneList.group1.temp <- geneList.group1 %>% dplyr::left_join(temp, by = c(\\gene\\))\n# TEMP END\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\npv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\npv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\npv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\npv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\npv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\npv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(temp, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() + \n    scale_y_continuous(breaks = seq(0, 30, by = 5), limits = c(0, 30)) + \n\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)  +\n  annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, pv12, \\\\n\\,\n                                                \\pv15: \\, pv15, \\\\n\\,\n                                                \\pv18: \\, pv18, \\\\n\\,\n                                                \\pv25: \\, pv25, \\\\n\\,\n                                                \\pv28: \\, pv28, \\\\n\\,\n                                                \\pv58: \\, pv58, \\\\n\\), \n           color = \\black\\, hjust = 0, size = 1) +  theme(legend.position = \\none\\)\n\n\nfileName <- paste0(\\countHub_barplot_diffGroup_dTAG_vs_DMSO\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# IMPORTING GENE ANNO DATA FOR P-N LOOPS\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                 diffCutoff = 0.2,\n                                 annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27, 28))\n\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>%\n  dplyr::mutate(binSize = end1 - start1,\n                adj_start1 = ifelse(binSize == 5000, start1,\n                                    ifelse(binSize == 10000, start1,\n                                           start1 + 10000)),\n                adj_end1 = adj_start1 + 5000,\n                adj_start2 = ifelse(binSize == 5000, start2,\n                                    ifelse(binSize == 10000, start2,\n                                           start2 + 10000)),\n                adj_end2 = adj_start2 + 5000,\n                anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \\_\\),\n                anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \\_\\))\n\n# data <- fread(here(consensusDir, paste0(name, \\.tsv\\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>% \n#   dplyr::mutate(binSize = end1 - start1,\n#                 adj_start1 = ifelse(binSize == 5000, start1,\n#                                     ifelse(binSize == 10000, start1+5000,\n#                                            start1 + 10000)),\n#                 adj_end1 = adj_start1 + 5000,\n#                 adj_start2 = ifelse(binSize == 5000, start2,\n#                                     ifelse(binSize == 10000, start2 + 5000,\n#                                            start2 + 10000)),\n#                 adj_end2 = adj_start2 + 5000,\n#                 anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \\_\\),\n#                 anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \\_\\))\n\ndata.reg <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\, \\E-E\\, \\E-S\\, \\E-X\\))\n\n## Graph\n#install.packages(\\igraph\\)\nlibrary(igraph)\n\n# To be able to construct a graph, each anchor need to have same resolution.\n# Shink the size to 10 kb\n# If the anchor is 25kb, choose the middle 5kb bin\n# If the anchor is 10kb, choose the left 5kb bin\n\ngraph <- graph_from_data_frame(data.reg %>% dplyr::select(anchor1, anchor2))\ncomponents <- components(graph)\ndata.reg$hub <- components$membership[data.reg$anchor1]\n\ntemp.bedpe <- data.reg %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)  \n\nhubNum <- data.reg %>% group_by(hub) %>% summarise(count = n())\n\n# Hub size distribution\np <- ggplot(hubNum, aes(x = count)) + geom_histogram(binwidth  = 1) + theme_classic() + \n  scale_y_log10()\n\nsvglite(here(figDir, \\hub_size_distribution.svg\\), height = 2, width = 2)\nprint(p)\ndev.off()\n\ndata.reg <- data.reg %>% dplyr::left_join(hubNum, by = \\hub\\) \ndata.reg <- data.reg %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n# Exporting regulatory loops with specific hub size\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 3) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub3.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)  \n\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 5) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub5.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)\n\ntemp.bedpe <- data.reg %>% dplyr::filter(count >= 10) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)\nfwrite(temp.bedpe, here(consensusDir, paste0(name, \\_regulatory_5kbAdj_hub10.bedpe\\)), \n       col.names = FALSE,row.names = FALSE, sep = \\\\t\\)\n\n\n# Adding gene annotation to hubs\ngenePeakPair <- geneAnnoData %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \\_\\)) %>% dplyr::select(peakID, gene)\ndata.reg <- data.reg %>% dplyr::left_join(genePeakPair, by = \\peakID\\)\n\n# During unnesting, E-N loops without gene annotated are removed\ndata.reg <- data.reg %>% unnest(gene) %>% dplyr::mutate(\n  group = ifelse(gene %in% gene.group1, \\group1\\,\n                 ifelse(gene %in% gene.group2, \\group2\\,\n                        ifelse(gene %in% gene.group3, \\group3\\,\n                               ifelse(gene %in% gene.group4, \\group4\\,\n                                      ifelse(gene %in% gene.group5, \\group5\\,\n                                             ifelse(gene %in% gene.group6, \\group6\\,\n                                                    ifelse(gene %in% gene.group7, \\group7\\,\n                                                           ifelse(gene %in% gene.group8, \\group8\\,\n                                                                  ifelse(gene %in% gene.group9, \\group9\\, NA)))))))))\n)\n\n\ntemp <- data.reg %>% dplyr::select(gene, hub, count, group) %>%\n  group_by(gene) %>% slice_max(count, with_ties = FALSE) %>%\n  dplyr::filter(group %in% c(\\group1\\, \\group2\\, \\group5\\, \\group8\\))\n\n\n# TEMP START\n# TEMP START\ngeneList.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))\ngeneList.group1.temp <- geneList.group1 %>% dplyr::left_join(temp, by = c(\\gene\\))\n# TEMP END\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$count\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$count\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\npv12 <- round(getPvalWilcox(tempSum,\\group1\\, \\group2\\), 5)\npv15 <- round(getPvalWilcox(tempSum,\\group1\\, \\group5\\), 5)\npv18 <- round(getPvalWilcox(tempSum,\\group1\\, \\group8\\), 5)\npv25 <- round(getPvalWilcox(tempSum,\\group2\\, \\group5\\), 5)\npv28 <- round(getPvalWilcox(tempSum,\\group2\\, \\group8\\), 5)\npv58 <- round(getPvalWilcox(tempSum,\\group5\\, \\group8\\), 5)\n\n\np <- ggplot(temp, aes(x = group, y = count, fill = group)) + \n  geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() + \n    scale_y_continuous(breaks = seq(0, 30, by = 5), limits = c(0, 30)) + \n\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)  +\n  annotate(\\text\\, x = 1, y = 3, label = paste0(\\pv12: \\, pv12, \\\\n\\,\n                                                \\pv15: \\, pv15, \\\\n\\,\n                                                \\pv18: \\, pv18, \\\\n\\,\n                                                \\pv25: \\, pv25, \\\\n\\,\n                                                \\pv28: \\, pv28, \\\\n\\,\n                                                \\pv58: \\, pv58, \\\\n\\), \n           color = \\black\\, hjust = 0, size = 1) +  theme(legend.position = \\none\\)\n\n\nfileName <- paste0(\\countHub_barplot_diffGroup_dTAG_vs_DMSO\\)\nheight <- 3\nwidth <- 3\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n```\n```"} -->
```r
```r
# IMPORTING GENE ANNO DATA FOR P-N LOOPS
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\)) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27, 28))
data <- fread(here(consensusDir, paste0(name, \.tsv\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>%
dplyr::mutate(binSize = end1 - start1,
adj_start1 = ifelse(binSize == 5000, start1,
ifelse(binSize == 10000, start1,
start1 + 10000)),
adj_end1 = adj_start1 + 5000,
adj_start2 = ifelse(binSize == 5000, start2,
ifelse(binSize == 10000, start2,
start2 + 10000)),
adj_end2 = adj_start2 + 5000,
anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \_\),
anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \_\))
# data <- fread(here(consensusDir, paste0(name, \.tsv\))) %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 13, 22, 23, 24, 25, 27)) %>%
# dplyr::mutate(binSize = end1 - start1,
# adj_start1 = ifelse(binSize == 5000, start1,
# ifelse(binSize == 10000, start1+5000,
# start1 + 10000)),
# adj_end1 = adj_start1 + 5000,
# adj_start2 = ifelse(binSize == 5000, start2,
# ifelse(binSize == 10000, start2 + 5000,
# start2 + 10000)),
# adj_end2 = adj_start2 + 5000,
# anchor1 = paste(chrom1, adj_start1, adj_end1, sep = \_\),
# anchor2 = paste(chrom2, adj_start2, adj_end2, sep = \_\))
data.reg <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \P-S\, \P-X\, \E-E\, \E-S\, \E-X\))
## Graph
#install.packages(\igraph\)
library(igraph)
# To be able to construct a graph, each anchor need to have same resolution.
# Shink the size to 10 kb
# If the anchor is 25kb, choose the middle 5kb bin
# If the anchor is 10kb, choose the left 5kb bin
graph <- graph_from_data_frame(data.reg %>% dplyr::select(anchor1, anchor2))
components <- components(graph)
data.reg$hub <- components$membership[data.reg$anchor1]
temp.bedpe <- data.reg %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)
fwrite(temp.bedpe, here(consensusDir, paste0(name, \_regulatory_5kbAdj.bedpe\)),
col.names = FALSE,row.names = FALSE, sep = \\t\)
hubNum <- data.reg %>% group_by(hub) %>% summarise(count = n())
# Hub size distribution
p <- ggplot(hubNum, aes(x = count)) + geom_histogram(binwidth = 1) + theme_classic() +
scale_y_log10()
svglite(here(figDir, \hub_size_distribution.svg\), height = 2, width = 2)
print(p)
dev.off()
data.reg <- data.reg %>% dplyr::left_join(hubNum, by = \hub\)
data.reg <- data.reg %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \_\))
# Exporting regulatory loops with specific hub size
temp.bedpe <- data.reg %>% dplyr::filter(count >= 3) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)
fwrite(temp.bedpe, here(consensusDir, paste0(name, \_regulatory_5kbAdj_hub3.bedpe\)),
col.names = FALSE,row.names = FALSE, sep = \\t\)
temp.bedpe <- data.reg %>% dplyr::filter(count >= 5) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)
fwrite(temp.bedpe, here(consensusDir, paste0(name, \_regulatory_5kbAdj_hub5.bedpe\)),
col.names = FALSE,row.names = FALSE, sep = \\t\)
temp.bedpe <- data.reg %>% dplyr::filter(count >= 10) %>% dplyr::select(chrom1, adj_start1, adj_end1, chrom2, adj_start2, adj_end2)
fwrite(temp.bedpe, here(consensusDir, paste0(name, \_regulatory_5kbAdj_hub10.bedpe\)),
col.names = FALSE,row.names = FALSE, sep = \\t\)
# Adding gene annotation to hubs
genePeakPair <- geneAnnoData %>% dplyr::mutate(peakID = paste(chrom1, start1, start2, sep = \_\)) %>% dplyr::select(peakID, gene)
data.reg <- data.reg %>% dplyr::left_join(genePeakPair, by = \peakID\)
# During unnesting, E-N loops without gene annotated are removed
data.reg <- data.reg %>% unnest(gene) %>% dplyr::mutate(
group = ifelse(gene %in% gene.group1, \group1\,
ifelse(gene %in% gene.group2, \group2\,
ifelse(gene %in% gene.group3, \group3\,
ifelse(gene %in% gene.group4, \group4\,
ifelse(gene %in% gene.group5, \group5\,
ifelse(gene %in% gene.group6, \group6\,
ifelse(gene %in% gene.group7, \group7\,
ifelse(gene %in% gene.group8, \group8\,
ifelse(gene %in% gene.group9, \group9\, NA)))))))))
)
temp <- data.reg %>% dplyr::select(gene, hub, count, group) %>%
group_by(gene) %>% slice_max(count, with_ties = FALSE) %>%
dplyr::filter(group %in% c(\group1\, \group2\, \group5\, \group8\))
# TEMP START
# TEMP START
geneList.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))
geneList.group1.temp <- geneList.group1 %>% dplyr::left_join(temp, by = c(\gene\))
# TEMP END
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$count
distance2 <- (data %>% dplyr::filter(group ==group2) )$count
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv12 <- round(getPvalWilcox(tempSum,\group1\, \group2\), 5)
pv15 <- round(getPvalWilcox(tempSum,\group1\, \group5\), 5)
pv18 <- round(getPvalWilcox(tempSum,\group1\, \group8\), 5)
pv25 <- round(getPvalWilcox(tempSum,\group2\, \group5\), 5)
pv28 <- round(getPvalWilcox(tempSum,\group2\, \group8\), 5)
pv58 <- round(getPvalWilcox(tempSum,\group5\, \group8\), 5)
p <- ggplot(temp, aes(x = group, y = count, fill = group)) +
geom_boxplot(width = 0.5, outlier.shape = NA) + theme_classic() +
scale_y_continuous(breaks = seq(0, 30, by = 5), limits = c(0, 30)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 3, label = paste0(\pv12: \, pv12, \\n\,
\pv15: \, pv15, \\n\,
\pv18: \, pv18, \\n\,
\pv25: \, pv25, \\n\,
\pv28: \, pv28, \\n\,
\pv58: \, pv58, \\n\),
color = \black\, hjust = 0, size = 1) + theme(legend.position = \none\)
fileName <- paste0(\countHub_barplot_diffGroup_dTAG_vs_DMSO\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### log2FC
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
# Import insulation score calculated with python
resultDir <- here(\../../result\)
insScore.DMSO <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1DMSO.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.DMSO) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
insScore.dTAG <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1dTAG.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.dTAG) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
#insScore <- full_join(insScore.DMSO, insScore.dTAG, by = c(\binID\)) %>%
# dplyr::select(binID, chrom.x, start.x, end.x, insulationScore.x, insulationScore.y)
#colnames(insScore) <- c(\binID\, \chr\, \start\, \end\, \insulation_score_DMSO\, \insulation_scoare_dTAG\)
#View(insScore)
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dUl5QkpiWEJ2Y25RZ2FXNXpkV3hoZEdsdmJpQnpZMjl5WlNCallXeGpkV3hoZEdWa0lIZHBkR2dnY0hsMGFHOXVYRzV5WlhOMWJIUkVhWElnUEMwZ2FHVnlaU2hjWEM0dUx5NHVMM0psYzNWc2RGeGNLVnh1YVc1elUyTnZjbVV1UkUxVFR5QThMU0JtY21WaFpDaG9aWEpsS0hKbGMzVnNkRVJwY2l3Z1hGeFVRVVJjWEN3Z1hGeHBibk4xYkdGMGFXOXVVMk52Y21WZk1qVnJZbDlITVVSTlUwOHVkSE4yWEZ3cEtTQWxQaVVnWEc0Z0lHUndiSGx5T2pwelpXeGxZM1FvWXloY1hHTm9jbTl0WEZ3c0lGeGNjM1JoY25SY1hDd2dYRnhsYm1SY1hDd2dYRnhzYjJjeVgybHVjM1ZzWVhScGIyNWZjMk52Y21WZk1USTFNREF3WEZ3cEtTQWxQaVZjYmlBZ1pIQnNlWEk2T20xMWRHRjBaU2hpYVc1SlJDQTlJSEJoYzNSbEtHTm9jbTl0TENCemRHRnlkQ3dnWlc1a0xDQnpaWEFnUFNCY1hGOWNYQ2twWEc1amIyeHVZVzFsY3locGJuTlRZMjl5WlM1RVRWTlBLU0E4TFNCaktGeGNZMmh5YjIxY1hDd2dYRnh6ZEdGeWRGeGNMQ0JjWEdWdVpGeGNMQ0JjWEdsdWMzVnNZWFJwYjI1VFkyOXlaVnhjTENCY1hHSnBia2xFWEZ3cFhHNXBibk5UWTI5eVpTNWtWRUZISUR3dElHWnlaV0ZrS0dobGNtVW9jbVZ6ZFd4MFJHbHlMQ0JjWEZSQlJGeGNMQ0JjWEdsdWMzVnNZWFJwYjI1VFkyOXlaVjh5Tld0aVgwY3haRlJCUnk1MGMzWmNYQ2twSUNVK0pTQmNiaUFnWkhCc2VYSTZPbk5sYkdWamRDaGpLRnhjWTJoeWIyMWNYQ3dnWEZ4emRHRnlkRnhjTENCY1hHVnVaRnhjTENCY1hHeHZaekpmYVc1emRXeGhkR2x2Ymw5elkyOXlaVjh4TWpVd01EQmNYQ2twSUNVK0pWeHVJQ0JrY0d4NWNqbzZiWFYwWVhSbEtHSnBia2xFSUQwZ2NHRnpkR1VvWTJoeWIyMHNJSE4wWVhKMExDQmxibVFzSUhObGNDQTlJRnhjWDF4Y0tTbGNibU52Ykc1aGJXVnpLR2x1YzFOamIzSmxMbVJVUVVjcElEd3RJR01vWEZ4amFISnZiVnhjTENCY1hITjBZWEowWEZ3c0lGeGNaVzVrWEZ3c0lGeGNhVzV6ZFd4aGRHbHZibE5qYjNKbFhGd3NJRnhjWW1sdVNVUmNYQ2xjYmx4dUkybHVjMU5qYjNKbElEd3RJR1oxYkd4ZmFtOXBiaWhwYm5OVFkyOXlaUzVFVFZOUExDQnBibk5UWTI5eVpTNWtWRUZITENCaWVTQTlJR01vWEZ4aWFXNUpSRnhjS1NrZ0pUNGxYRzRqSUNCa2NHeDVjam82YzJWc1pXTjBLR0pwYmtsRUxDQmphSEp2YlM1NExDQnpkR0Z5ZEM1NExDQmxibVF1ZUN3Z2FXNXpkV3hoZEdsdmJsTmpiM0psTG5nc0lHbHVjM1ZzWVhScGIyNVRZMjl5WlM1NUtTQmNibHh1STJOdmJHNWhiV1Z6S0dsdWMxTmpiM0psS1NBOExTQmpLRnhjWW1sdVNVUmNYQ3dnWEZ4amFISmNYQ3dnWEZ4emRHRnlkRnhjTENCY1hHVnVaRnhjTENCY1hHbHVjM1ZzWVhScGIyNWZjMk52Y21WZlJFMVRUMXhjTENCY1hHbHVjM1ZzWVhScGIyNWZjMk52WVhKbFgyUlVRVWRjWENsY2JseHVJMVpwWlhjb2FXNXpVMk52Y21VcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG4jIEltcG9ydCBpbnN1bGF0aW9uIHNjb3JlIGNhbGN1bGF0ZWQgd2l0aCBweXRob25cbnJlc3VsdERpciA8LSBoZXJlKFxcLi4vLi4vcmVzdWx0XFwpXG5pbnNTY29yZS5ETVNPIDwtIGZyZWFkKGhlcmUocmVzdWx0RGlyLCBcXFRBRFxcLCBcXGluc3VsYXRpb25TY29yZV8yNWtiX0cxRE1TTy50c3ZcXCkpICU+JSBcbiAgZHBseXI6OnNlbGVjdChjKFxcY2hyb21cXCwgXFxzdGFydFxcLCBcXGVuZFxcLCBcXGxvZzJfaW5zdWxhdGlvbl9zY29yZV8xMjUwMDBcXCkpICU+JVxuICBkcGx5cjo6bXV0YXRlKGJpbklEID0gcGFzdGUoY2hyb20sIHN0YXJ0LCBlbmQsIHNlcCA9IFxcX1xcKSlcbmNvbG5hbWVzKGluc1Njb3JlLkRNU08pIDwtIGMoXFxjaHJvbVxcLCBcXHN0YXJ0XFwsIFxcZW5kXFwsIFxcaW5zdWxhdGlvblNjb3JlXFwsIFxcYmluSURcXClcbmluc1Njb3JlLmRUQUcgPC0gZnJlYWQoaGVyZShyZXN1bHREaXIsIFxcVEFEXFwsIFxcaW5zdWxhdGlvblNjb3JlXzI1a2JfRzFkVEFHLnRzdlxcKSkgJT4lIFxuICBkcGx5cjo6c2VsZWN0KGMoXFxjaHJvbVxcLCBcXHN0YXJ0XFwsIFxcZW5kXFwsIFxcbG9nMl9pbnN1bGF0aW9uX3Njb3JlXzEyNTAwMFxcKSkgJT4lXG4gIGRwbHlyOjptdXRhdGUoYmluSUQgPSBwYXN0ZShjaHJvbSwgc3RhcnQsIGVuZCwgc2VwID0gXFxfXFwpKVxuY29sbmFtZXMoaW5zU2NvcmUuZFRBRykgPC0gYyhcXGNocm9tXFwsIFxcc3RhcnRcXCwgXFxlbmRcXCwgXFxpbnN1bGF0aW9uU2NvcmVcXCwgXFxiaW5JRFxcKVxuXG4jaW5zU2NvcmUgPC0gZnVsbF9qb2luKGluc1Njb3JlLkRNU08sIGluc1Njb3JlLmRUQUcsIGJ5ID0gYyhcXGJpbklEXFwpKSAlPiVcbiMgIGRwbHlyOjpzZWxlY3QoYmluSUQsIGNocm9tLngsIHN0YXJ0LngsIGVuZC54LCBpbnN1bGF0aW9uU2NvcmUueCwgaW5zdWxhdGlvblNjb3JlLnkpIFxuXG4jY29sbmFtZXMoaW5zU2NvcmUpIDwtIGMoXFxiaW5JRFxcLCBcXGNoclxcLCBcXHN0YXJ0XFwsIFxcZW5kXFwsIFxcaW5zdWxhdGlvbl9zY29yZV9ETVNPXFwsIFxcaW5zdWxhdGlvbl9zY29hcmVfZFRBR1xcKVxuXG4jVmlldyhpbnNTY29yZSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
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```r
```r
# Import insulation score calculated with python
resultDir <- here(\../../result\)
insScore.DMSO <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1DMSO.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.DMSO) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
insScore.dTAG <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1dTAG.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.dTAG) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
#insScore <- full_join(insScore.DMSO, insScore.dTAG, by = c(\binID\)) %>%
# dplyr::select(binID, chrom.x, start.x, end.x, insulationScore.x, insulationScore.y)
#colnames(insScore) <- c(\binID\, \chr\, \start\, \end\, \insulation_score_DMSO\, \insulation_scoare_dTAG\)
#View(insScore)
```
```
<!-- rnb-source-end -->
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## [2.13] How to define multiconnected hub using regulatory loops?
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gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, V5, V1, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \gene\, \chr\, \TSS\)
getInsulationScore <- function(chr, TSS, insScore.tb){
temp.tb <- insScore.tb %>% dplyr::filter(chrom == chr, start < TSS, end > TSS)
out <- temp.tb$insulationScore
if(length(out) < 1){
return(NA)
}else{
return(temp.tb$insulationScore)
}
}
gene.insScore.all <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
log2_insScore_DMSO = getInsulationScore(chr, TSS, insScore.DMSO),
insScore_DMSO = 2^log2_insScore_DMSO,
log2_insScore_dTAG = getInsulationScore(chr, TSS, insScore.dTAG),
insScore_dTAG = 2^log2_insScore_dTAG,
diff_insScore = insScore_dTAG - insScore_DMSO)
<!-- rnb-output-end -->
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```r
```r
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, V5, V1, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \gene\, \chr\, \TSS\)
getInsulationScore <- function(chr, TSS, insScore.tb){
temp.tb <- insScore.tb %>% dplyr::filter(chrom == chr, start < TSS, end > TSS)
out <- temp.tb$insulationScore
if(length(out) < 1){
return(NA)
}else{
return(temp.tb$insulationScore)
}
}
gene.insScore.all <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
log2_insScore_DMSO = getInsulationScore(chr, TSS, insScore.DMSO),
insScore_DMSO = 2^log2_insScore_DMSO,
log2_insScore_dTAG = getInsulationScore(chr, TSS, insScore.dTAG),
insScore_dTAG = 2^log2_insScore_dTAG,
diff_insScore = insScore_dTAG - insScore_DMSO)
```
```
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<!-- rnb-text-begin -->
## [2.14] Looking deeper into TAD and insulation
### Insulation score of TSS?
#### Importing insulation score
<!-- rnb-text-end -->
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# Importing TAD boundaries
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
center = (start + end)/2)
findClosestTADBoundary <- function(chrom, TSS, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom) %>%
dplyr::mutate(distance = abs(center - TSS)) %>%
slice_min(distance)
if(nrow(temp) == 1){
return(temp$center)
}else{
return(NA)
}
}
gene.insScore.all <- gene.insScore.all %>% rowwise() %>%
dplyr::mutate(closestBoundary = findClosestTADBoundary(chr, TSS, tad_boundary),
log2_boundaryInsScore_DMSO = getInsulationScore(chr, closestBoundary, insScore.DMSO),
boundaryInsScore_DMSO = 2^log2_boundaryInsScore_DMSO,
log2_boundaryInsScore_dTAG = getInsulationScore(chr, closestBoundary, insScore.dTAG),
boundaryInsScore_dTAG = 2^log2_boundaryInsScore_dTAG,
diff_boundaryInsScore = boundaryInsScore_dTAG - boundaryInsScore_DMSO)
saveRDS(gene.insScore.all, here(resultDir, \gene.insScore.all.rds\))
gene.insScore.all <- readRDS(here(resultDir, \gene.insScore.all.rds\))
<!-- rnb-output-end -->
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```r
```r
# Importing TAD boundaries
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
center = (start + end)/2)
findClosestTADBoundary <- function(chrom, TSS, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom) %>%
dplyr::mutate(distance = abs(center - TSS)) %>%
slice_min(distance)
if(nrow(temp) == 1){
return(temp$center)
}else{
return(NA)
}
}
gene.insScore.all <- gene.insScore.all %>% rowwise() %>%
dplyr::mutate(closestBoundary = findClosestTADBoundary(chr, TSS, tad_boundary),
log2_boundaryInsScore_DMSO = getInsulationScore(chr, closestBoundary, insScore.DMSO),
boundaryInsScore_DMSO = 2^log2_boundaryInsScore_DMSO,
log2_boundaryInsScore_dTAG = getInsulationScore(chr, closestBoundary, insScore.dTAG),
boundaryInsScore_dTAG = 2^log2_boundaryInsScore_dTAG,
diff_boundaryInsScore = boundaryInsScore_dTAG - boundaryInsScore_DMSO)
saveRDS(gene.insScore.all, here(resultDir, \gene.insScore.all.rds\))
gene.insScore.all <- readRDS(here(resultDir, \gene.insScore.all.rds\))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Linking to gene
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\, NA))) %>% \n  dplyr::filter(!is.na(group))\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_insScore <- function(temp.tb, note, ymin = -1.5, ymax = 0){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = -score)) + \n    labs(x = NULL, y = \\- Insulation score\\) +  # Remove x-axis title\n    scale_fill_manual(values = rev((c(\\#777777\\, \\#F28E2C\\)))) +\n\n    introdataviz::geom_split_violin(linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                                    alpha = .4) +\n    geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + theme_classic() +\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # \n    # geom_violin(aes(fill = group), color = \\black\\, linewidth = lineThick*mmToLineUnit, lineend = \\square\\, show.legend = FALSE) + \n    # geom_boxplot(width = 0.1,  color = \\black\\, linewidth = lineThick*mmToLineUnit, lineend = \\square\\,\n    #              outlier.size = 1, outlier.stroke = NA) +\n    # stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 1, fill = \\red\\, color = \\black\\) + \n    geom_hline(yintercept = 0, linewidth = lineThick*mmToLineUnit) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p12: \\, convPvalue(p12)),\n             color = \\black\\, hjust = 0, size = 3) +\n    theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )+   guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      ))\n      \n      \n  fileName <- paste0(\\insulation_score_binaryGroup_\\, note)\n  width <- panelSize(1.55)*mmToInch\nheight <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_binaryGroup\\, ymin = -1.1, ymax = -0.5)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_binaryGroup\\, ymin =  -1.1, ymax = -0.5)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_binaryGroup\\, ymin = -0.3, ymax = 0.1)\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\, NA))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = -1.5, ymax = 0){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = \.\, fill = group, y = -score)) +
labs(x = NULL, y = \- Insulation score\) + # Remove x-axis title
scale_fill_manual(values = rev((c(\#777777\, \#F28E2C\)))) +
introdataviz::geom_split_violin(linewidth = lineMedium * mmToLineUnit, lineend = \square\,
alpha = .4) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) + theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
#
# geom_violin(aes(fill = group), color = \black\, linewidth = lineThick*mmToLineUnit, lineend = \square\, show.legend = FALSE) +
# geom_boxplot(width = 0.1, color = \black\, linewidth = lineThick*mmToLineUnit, lineend = \square\,
# outlier.size = 1, outlier.stroke = NA) +
# stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 1, fill = \red\, color = \black\) +
geom_hline(yintercept = 0, linewidth = lineThick*mmToLineUnit) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))
fileName <- paste0(\insulation_score_binaryGroup_\, note)
width <- panelSize(1.55)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_binaryGroup\, ymin = -1.1, ymax = -0.5)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_binaryGroup\, ymin = -1.1, ymax = -0.5)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_binaryGroup\, ymin = -0.3, ymax = 0.1)
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\, NA))) %>% \n  dplyr::filter(!is.na(group))\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_insScore <- function(temp.tb, note, ymin = -1.5, ymax = 0){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = -score)) + \n    labs(x = NULL, y = \\- Insulation score\\) +  # Remove x-axis title\n    scale_fill_manual(values = rev((c(\\#777777\\, \\#F28E2C\\)))) +\n\n    introdataviz::geom_split_violin(linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                                    alpha = .4) +\n    geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + theme_classic() +\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # \n    # geom_violin(aes(fill = group), color = \\black\\, linewidth = lineThick*mmToLineUnit, lineend = \\square\\, show.legend = FALSE) + \n    # geom_boxplot(width = 0.1,  color = \\black\\, linewidth = lineThick*mmToLineUnit, lineend = \\square\\,\n    #              outlier.size = 1, outlier.stroke = NA) +\n    # stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 1, fill = \\red\\, color = \\black\\) + \n    geom_hline(yintercept = 0, linewidth = lineThick*mmToLineUnit) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p12: \\, convPvalue(p12)),\n             color = \\black\\, hjust = 0, size = 3) +\n    theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )+   guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      ))\n      \n      \n  fileName <- paste0(\\insulation_score_binaryGroup_\\, note)\n  width <- panelSize(1.55)*mmToInch\nheight <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_binaryGroup\\, ymin = -1.1, ymax = -0.5)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_binaryGroup\\, ymin =  -1.1, ymax = -0.5)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_binaryGroup\\, ymin = -0.3, ymax = 0.1)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\, NA))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = -1.5, ymax = 0){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = \.\, fill = group, y = -score)) +
labs(x = NULL, y = \- Insulation score\) + # Remove x-axis title
scale_fill_manual(values = rev((c(\#777777\, \#F28E2C\)))) +
introdataviz::geom_split_violin(linewidth = lineMedium * mmToLineUnit, lineend = \square\,
alpha = .4) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) + theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
#
# geom_violin(aes(fill = group), color = \black\, linewidth = lineThick*mmToLineUnit, lineend = \square\, show.legend = FALSE) +
# geom_boxplot(width = 0.1, color = \black\, linewidth = lineThick*mmToLineUnit, lineend = \square\,
# outlier.size = 1, outlier.stroke = NA) +
# stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 1, fill = \red\, color = \black\) +
geom_hline(yintercept = 0, linewidth = lineThick*mmToLineUnit) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))
fileName <- paste0(\insulation_score_binaryGroup_\, note)
width <- panelSize(1.55)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_binaryGroup\, ymin = -1.1, ymax = -0.5)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_binaryGroup\, ymin = -1.1, ymax = -0.5)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_binaryGroup\, ymin = -0.3, ymax = 0.1)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Checking insulation score of the nearest TAD
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\,\n                        ifelse(ensembl %in% group5, \\group5\\,\n                               ifelse(ensembl %in% group8, \\group8\\, NA))))) %>% \n  dplyr::filter(!is.na(group))\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff\\, ymin = -0.5, ymax = 1)\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff\, ymin = -0.5, ymax = 1)
<!-- rnb-output-end -->
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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\,\n                        ifelse(ensembl %in% group5, \\group5\\,\n                               ifelse(ensembl %in% group8, \\group8\\, NA))))) %>% \n  dplyr::filter(!is.na(group))\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff\\, ymin = -0.5, ymax = 1)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff\, ymin = -0.5, ymax = 1)
```
```
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<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
##### Plotting for binary groups
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene\npnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene\npnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene\npnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene\npnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% pnOver8, \\p8\\,\n                 ifelse(ensembl %in% pnOver6, \\p6\\,\n                        ifelse(ensembl %in% pnOver4, \\p4\\,\n                               ifelse(ensembl %in% pnOver2, \\p2\\, \n                                      ifelse(ensembl %in% pnOver0, \\p0\\, NA))))))  %>% \n  dplyr::filter(!is.na(group))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p02 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p2\\), 5)\n  p24 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p4\\), 5)\n  p46 <- round(getPvalWilcox(temp.tb, \\p4\\, \\p6\\), 5)\n  p68 <- round(getPvalWilcox(temp.tb, \\p6\\, \\p8\\), 5)\n  p48 <- round(getPvalWilcox(temp.tb, \\p4\\, \\p8\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p8\\), 5)\n  p08 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p02: \\, convPvalue(p02), \\\\n\\,\n                                                         \\p24: \\, convPvalue(p24), \\\\n\\,\n                                                         \\p46: \\, convPvalue(p46), \\\\n\\,\n                                                         \\p68: \\, convPvalue(p68), \\\\n\\,\n                                                         \\p48: \\, convPvalue(p48), \\\\n\\,\n                                                         \\p28: \\,convPvalue( p28), \\\\n\\,\n                                                         \\p08: \\,convPvalue( p08), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)  +\n    scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_P-N\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_P-N\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_P-N\\, ymin = -0.5, ymax = 1)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
pnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene
pnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene
pnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene
pnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene
pnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% pnOver8, \p8\,
ifelse(ensembl %in% pnOver6, \p6\,
ifelse(ensembl %in% pnOver4, \p4\,
ifelse(ensembl %in% pnOver2, \p2\,
ifelse(ensembl %in% pnOver0, \p0\, NA)))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p02 <- round(getPvalWilcox(temp.tb, \p0\, \p2\), 5)
p24 <- round(getPvalWilcox(temp.tb, \p2\, \p4\), 5)
p46 <- round(getPvalWilcox(temp.tb, \p4\, \p6\), 5)
p68 <- round(getPvalWilcox(temp.tb, \p6\, \p8\), 5)
p48 <- round(getPvalWilcox(temp.tb, \p4\, \p8\), 5)
p28 <- round(getPvalWilcox(temp.tb, \p2\, \p8\), 5)
p08 <- round(getPvalWilcox(temp.tb, \p0\, \p8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p02: \, convPvalue(p02), \\n\,
\p24: \, convPvalue(p24), \\n\,
\p46: \, convPvalue(p46), \\n\,
\p68: \, convPvalue(p68), \\n\,
\p48: \, convPvalue(p48), \\n\,
\p28: \,convPvalue( p28), \\n\,
\p08: \,convPvalue( p08), \\n\),
color = \black\, hjust = 0, size = 3) +
scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_P-N\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_P-N\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_P-N\, ymin = -0.5, ymax = 1)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene\npnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene\npnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene\npnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene\npnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% pnOver8, \\p8\\,\n                 ifelse(ensembl %in% pnOver6, \\p6\\,\n                        ifelse(ensembl %in% pnOver4, \\p4\\,\n                               ifelse(ensembl %in% pnOver2, \\p2\\, \n                                      ifelse(ensembl %in% pnOver0, \\p0\\, NA))))))  %>% \n  dplyr::filter(!is.na(group))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p02 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p2\\), 5)\n  p24 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p4\\), 5)\n  p46 <- round(getPvalWilcox(temp.tb, \\p4\\, \\p6\\), 5)\n  p68 <- round(getPvalWilcox(temp.tb, \\p6\\, \\p8\\), 5)\n  p48 <- round(getPvalWilcox(temp.tb, \\p4\\, \\p8\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p8\\), 5)\n  p08 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p02: \\, convPvalue(p02), \\\\n\\,\n                                                         \\p24: \\, convPvalue(p24), \\\\n\\,\n                                                         \\p46: \\, convPvalue(p46), \\\\n\\,\n                                                         \\p68: \\, convPvalue(p68), \\\\n\\,\n                                                         \\p48: \\, convPvalue(p48), \\\\n\\,\n                                                         \\p28: \\,convPvalue( p28), \\\\n\\,\n                                                         \\p08: \\,convPvalue( p08), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)  +\n    scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_P-N\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_P-N\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_P-N\\, ymin = -0.5, ymax = 1)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
pnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene
pnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene
pnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene
pnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene
pnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% pnOver8, \p8\,
ifelse(ensembl %in% pnOver6, \p6\,
ifelse(ensembl %in% pnOver4, \p4\,
ifelse(ensembl %in% pnOver2, \p2\,
ifelse(ensembl %in% pnOver0, \p0\, NA)))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p02 <- round(getPvalWilcox(temp.tb, \p0\, \p2\), 5)
p24 <- round(getPvalWilcox(temp.tb, \p2\, \p4\), 5)
p46 <- round(getPvalWilcox(temp.tb, \p4\, \p6\), 5)
p68 <- round(getPvalWilcox(temp.tb, \p6\, \p8\), 5)
p48 <- round(getPvalWilcox(temp.tb, \p4\, \p8\), 5)
p28 <- round(getPvalWilcox(temp.tb, \p2\, \p8\), 5)
p08 <- round(getPvalWilcox(temp.tb, \p0\, \p8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p02: \, convPvalue(p02), \\n\,
\p24: \, convPvalue(p24), \\n\,
\p46: \, convPvalue(p46), \\n\,
\p68: \, convPvalue(p68), \\n\,
\p48: \, convPvalue(p48), \\n\,
\p28: \,convPvalue( p28), \\n\,
\p08: \,convPvalue( p08), \\n\),
color = \black\, hjust = 0, size = 3) +
scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_P-N\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_P-N\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_P-N\, ymin = -0.5, ymax = 1)
```
```
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##### Plotting for groups
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% psOver4, \\p4\\,\n                 ifelse(ensembl %in% psOver3, \\p3\\,\n                        ifelse(ensembl %in% psOver2, \\p2\\,\n                               ifelse(ensembl %in% psOver1, \\p1\\, \n                                      ifelse(ensembl %in% psOver0, \\p0\\, NA))))))  %>% \n  dplyr::filter(!is.na(group))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p01 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p1\\), 5)\n  p12 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p2\\), 5)\n  p23 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p3\\), 5)\n  p34 <- round(getPvalWilcox(temp.tb, \\p3\\, \\p4\\), 5)\n  p24 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p4\\), 5)\n  p14 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p4\\), 5)\n  p04 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p4\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p01: \\, convPvalue(p01), \\\\n\\,\n                                                         \\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p23: \\, convPvalue(p23), \\\\n\\,\n                                                         \\p34: \\, convPvalue(p34), \\\\n\\,\n                                                         \\p24: \\, convPvalue(p24), \\\\n\\,\n                                                         \\p14: \\,convPvalue( p14), \\\\n\\,\n                                                         \\p04: \\,convPvalue( p04), \\\\n\\), \n             color = \\black\\, hjust = 0, size = 3) +\n    scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_P-S\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_P-S\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_P-S\\, ymin = -0.5, ymax = 1)\n```\n\n<!-- rnb-source-end -->\n"} -->
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% psOver4, \p4\,
ifelse(ensembl %in% psOver3, \p3\,
ifelse(ensembl %in% psOver2, \p2\,
ifelse(ensembl %in% psOver1, \p1\,
ifelse(ensembl %in% psOver0, \p0\, NA)))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p01 <- round(getPvalWilcox(temp.tb, \p0\, \p1\), 5)
p12 <- round(getPvalWilcox(temp.tb, \p1\, \p2\), 5)
p23 <- round(getPvalWilcox(temp.tb, \p2\, \p3\), 5)
p34 <- round(getPvalWilcox(temp.tb, \p3\, \p4\), 5)
p24 <- round(getPvalWilcox(temp.tb, \p2\, \p4\), 5)
p14 <- round(getPvalWilcox(temp.tb, \p1\, \p4\), 5)
p04 <- round(getPvalWilcox(temp.tb, \p0\, \p4\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p01: \, convPvalue(p01), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p34: \, convPvalue(p34), \\n\,
\p24: \, convPvalue(p24), \\n\,
\p14: \,convPvalue( p14), \\n\,
\p04: \,convPvalue( p04), \\n\),
color = \black\, hjust = 0, size = 3) +
scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_P-S\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_P-S\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_P-S\, ymin = -0.5, ymax = 1)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% psOver4, \\p4\\,\n                 ifelse(ensembl %in% psOver3, \\p3\\,\n                        ifelse(ensembl %in% psOver2, \\p2\\,\n                               ifelse(ensembl %in% psOver1, \\p1\\, \n                                      ifelse(ensembl %in% psOver0, \\p0\\, NA))))))  %>% \n  dplyr::filter(!is.na(group))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p01 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p1\\), 5)\n  p12 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p2\\), 5)\n  p23 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p3\\), 5)\n  p34 <- round(getPvalWilcox(temp.tb, \\p3\\, \\p4\\), 5)\n  p24 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p4\\), 5)\n  p14 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p4\\), 5)\n  p04 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p4\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p01: \\, convPvalue(p01), \\\\n\\,\n                                                         \\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p23: \\, convPvalue(p23), \\\\n\\,\n                                                         \\p34: \\, convPvalue(p34), \\\\n\\,\n                                                         \\p24: \\, convPvalue(p24), \\\\n\\,\n                                                         \\p14: \\,convPvalue( p14), \\\\n\\,\n                                                         \\p04: \\,convPvalue( p04), \\\\n\\), \n             color = \\black\\, hjust = 0, size = 3) +\n    scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_P-S\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_P-S\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_P-S\\, ymin = -0.5, ymax = 1)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% psOver4, \p4\,
ifelse(ensembl %in% psOver3, \p3\,
ifelse(ensembl %in% psOver2, \p2\,
ifelse(ensembl %in% psOver1, \p1\,
ifelse(ensembl %in% psOver0, \p0\, NA)))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p01 <- round(getPvalWilcox(temp.tb, \p0\, \p1\), 5)
p12 <- round(getPvalWilcox(temp.tb, \p1\, \p2\), 5)
p23 <- round(getPvalWilcox(temp.tb, \p2\, \p3\), 5)
p34 <- round(getPvalWilcox(temp.tb, \p3\, \p4\), 5)
p24 <- round(getPvalWilcox(temp.tb, \p2\, \p4\), 5)
p14 <- round(getPvalWilcox(temp.tb, \p1\, \p4\), 5)
p04 <- round(getPvalWilcox(temp.tb, \p0\, \p4\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p01: \, convPvalue(p01), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p34: \, convPvalue(p34), \\n\,
\p24: \, convPvalue(p24), \\n\,
\p14: \,convPvalue( p14), \\n\,
\p04: \,convPvalue( p04), \\n\),
color = \black\, hjust = 0, size = 3) +
scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_P-S\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_P-S\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_P-S\, ymin = -0.5, ymax = 1)
```
```
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##### Plotting for P-N
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% peOver4, \\p4\\,\n                 ifelse(ensembl %in% peOver3, \\p3\\,\n                        ifelse(ensembl %in% peOver2, \\p2\\,\n                               ifelse(ensembl %in% peOver1, \\p1\\, \n                                      ifelse(ensembl %in% peOver0, \\p0\\, NA))))))  %>% \n  dplyr::filter(!is.na(group))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p01 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p1\\), 5)\n  p12 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p2\\), 5)\n  p23 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p3\\), 5)\n  p34 <- round(getPvalWilcox(temp.tb, \\p3\\, \\p4\\), 5)\n  p24 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p4\\), 5)\n  p14 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p4\\), 5)\n  p04 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p4\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p01: \\, convPvalue(p01), \\\\n\\,\n                                                         \\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p23: \\, convPvalue(p23), \\\\n\\,\n                                                         \\p34: \\, convPvalue(p34), \\\\n\\,\n                                                         \\p24: \\, convPvalue(p24), \\\\n\\,\n                                                         \\p14: \\,convPvalue( p14), \\\\n\\,\n                                                         \\p04: \\,convPvalue( p04), \\\\n\\), \n             color = \\black\\, hjust = 0, size = 3) +\n    scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_P-E\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_P-E\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_P-E\\, ymin = -0.5, ymax = 1)\n```\n\n<!-- rnb-source-end -->\n"} -->
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% peOver4, \p4\,
ifelse(ensembl %in% peOver3, \p3\,
ifelse(ensembl %in% peOver2, \p2\,
ifelse(ensembl %in% peOver1, \p1\,
ifelse(ensembl %in% peOver0, \p0\, NA)))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p01 <- round(getPvalWilcox(temp.tb, \p0\, \p1\), 5)
p12 <- round(getPvalWilcox(temp.tb, \p1\, \p2\), 5)
p23 <- round(getPvalWilcox(temp.tb, \p2\, \p3\), 5)
p34 <- round(getPvalWilcox(temp.tb, \p3\, \p4\), 5)
p24 <- round(getPvalWilcox(temp.tb, \p2\, \p4\), 5)
p14 <- round(getPvalWilcox(temp.tb, \p1\, \p4\), 5)
p04 <- round(getPvalWilcox(temp.tb, \p0\, \p4\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p01: \, convPvalue(p01), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p34: \, convPvalue(p34), \\n\,
\p24: \, convPvalue(p24), \\n\,
\p14: \,convPvalue( p14), \\n\,
\p04: \,convPvalue( p04), \\n\),
color = \black\, hjust = 0, size = 3) +
scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_P-E\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_P-E\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_P-E\, ymin = -0.5, ymax = 1)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% peOver4, \\p4\\,\n                 ifelse(ensembl %in% peOver3, \\p3\\,\n                        ifelse(ensembl %in% peOver2, \\p2\\,\n                               ifelse(ensembl %in% peOver1, \\p1\\, \n                                      ifelse(ensembl %in% peOver0, \\p0\\, NA))))))  %>% \n  dplyr::filter(!is.na(group))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p01 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p1\\), 5)\n  p12 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p2\\), 5)\n  p23 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p3\\), 5)\n  p34 <- round(getPvalWilcox(temp.tb, \\p3\\, \\p4\\), 5)\n  p24 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p4\\), 5)\n  p14 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p4\\), 5)\n  p04 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p4\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p01: \\, convPvalue(p01), \\\\n\\,\n                                                         \\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p23: \\, convPvalue(p23), \\\\n\\,\n                                                         \\p34: \\, convPvalue(p34), \\\\n\\,\n                                                         \\p24: \\, convPvalue(p24), \\\\n\\,\n                                                         \\p14: \\,convPvalue( p14), \\\\n\\,\n                                                         \\p04: \\,convPvalue( p04), \\\\n\\), \n             color = \\black\\, hjust = 0, size = 3) +\n    scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_P-E\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_P-E\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_P-E\\, ymin = -0.5, ymax = 1)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% peOver4, \p4\,
ifelse(ensembl %in% peOver3, \p3\,
ifelse(ensembl %in% peOver2, \p2\,
ifelse(ensembl %in% peOver1, \p1\,
ifelse(ensembl %in% peOver0, \p0\, NA)))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p01 <- round(getPvalWilcox(temp.tb, \p0\, \p1\), 5)
p12 <- round(getPvalWilcox(temp.tb, \p1\, \p2\), 5)
p23 <- round(getPvalWilcox(temp.tb, \p2\, \p3\), 5)
p34 <- round(getPvalWilcox(temp.tb, \p3\, \p4\), 5)
p24 <- round(getPvalWilcox(temp.tb, \p2\, \p4\), 5)
p14 <- round(getPvalWilcox(temp.tb, \p1\, \p4\), 5)
p04 <- round(getPvalWilcox(temp.tb, \p0\, \p4\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p01: \, convPvalue(p01), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p34: \, convPvalue(p34), \\n\,
\p24: \, convPvalue(p24), \\n\,
\p14: \,convPvalue( p14), \\n\,
\p04: \,convPvalue( p04), \\n\),
color = \black\, hjust = 0, size = 3) +
scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_P-E\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_P-E\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_P-E\, ymin = -0.5, ymax = 1)
```
```
<!-- rnb-source-end -->
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<!-- rnb-text-begin -->
##### Plotting for P-S
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% ppOver4, \\p4\\,\n                 ifelse(ensembl %in% ppOver3, \\p3\\,\n                        ifelse(ensembl %in% ppOver2, \\p2\\,\n                               ifelse(ensembl %in% ppOver1, \\p1\\, \n                                      ifelse(ensembl %in% ppOver0, \\p0\\, NA))))))  %>% \n  dplyr::filter(!is.na(group))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p01 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p1\\), 5)\n  p12 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p2\\), 5)\n  p23 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p3\\), 5)\n  p34 <- round(getPvalWilcox(temp.tb, \\p3\\, \\p4\\), 5)\n  p24 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p4\\), 5)\n  p14 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p4\\), 5)\n  p04 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p4\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p01: \\, convPvalue(p01), \\\\n\\,\n                                                         \\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p23: \\, convPvalue(p23), \\\\n\\,\n                                                         \\p34: \\, convPvalue(p34), \\\\n\\,\n                                                         \\p24: \\, convPvalue(p24), \\\\n\\,\n                                                         \\p14: \\,convPvalue( p14), \\\\n\\,\n                                                         \\p04: \\,convPvalue( p04), \\\\n\\), \n             color = \\black\\, hjust = 0, size = 3) +\n    scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_P-P\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_P-P\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_P-P\\, ymin = -0.5, ymax = 1)\n```\n\n<!-- rnb-source-end -->\n"} -->
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% ppOver4, \p4\,
ifelse(ensembl %in% ppOver3, \p3\,
ifelse(ensembl %in% ppOver2, \p2\,
ifelse(ensembl %in% ppOver1, \p1\,
ifelse(ensembl %in% ppOver0, \p0\, NA)))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p01 <- round(getPvalWilcox(temp.tb, \p0\, \p1\), 5)
p12 <- round(getPvalWilcox(temp.tb, \p1\, \p2\), 5)
p23 <- round(getPvalWilcox(temp.tb, \p2\, \p3\), 5)
p34 <- round(getPvalWilcox(temp.tb, \p3\, \p4\), 5)
p24 <- round(getPvalWilcox(temp.tb, \p2\, \p4\), 5)
p14 <- round(getPvalWilcox(temp.tb, \p1\, \p4\), 5)
p04 <- round(getPvalWilcox(temp.tb, \p0\, \p4\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p01: \, convPvalue(p01), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p34: \, convPvalue(p34), \\n\,
\p24: \, convPvalue(p24), \\n\,
\p14: \,convPvalue( p14), \\n\,
\p04: \,convPvalue( p04), \\n\),
color = \black\, hjust = 0, size = 3) +
scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_P-P\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_P-P\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_P-P\, ymin = -0.5, ymax = 1)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n\ngene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% ppOver4, \\p4\\,\n                 ifelse(ensembl %in% ppOver3, \\p3\\,\n                        ifelse(ensembl %in% ppOver2, \\p2\\,\n                               ifelse(ensembl %in% ppOver1, \\p1\\, \n                                      ifelse(ensembl %in% ppOver0, \\p0\\, NA))))))  %>% \n  dplyr::filter(!is.na(group))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$score\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nplot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){\n  p01 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p1\\), 5)\n  p12 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p2\\), 5)\n  p23 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p3\\), 5)\n  p34 <- round(getPvalWilcox(temp.tb, \\p3\\, \\p4\\), 5)\n  p24 <- round(getPvalWilcox(temp.tb, \\p2\\, \\p4\\), 5)\n  p14 <- round(getPvalWilcox(temp.tb, \\p1\\, \\p4\\), 5)\n  p04 <- round(getPvalWilcox(temp.tb, \\p0\\, \\p4\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +\n    annotate(\\text\\, x = 1, y = ymin + 1, label = paste0(\\p01: \\, convPvalue(p01), \\\\n\\,\n                                                         \\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p23: \\, convPvalue(p23), \\\\n\\,\n                                                         \\p34: \\, convPvalue(p34), \\\\n\\,\n                                                         \\p24: \\, convPvalue(p24), \\\\n\\,\n                                                         \\p14: \\,convPvalue( p14), \\\\n\\,\n                                                         \\p04: \\,convPvalue( p04), \\\\n\\), \n             color = \\black\\, hjust = 0, size = 3) +\n    scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n  \n  \n  fileName <- paste0(\\insulation_score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# DMSO\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_DMSO_P-P\\)\n\n# dTAG\ntemp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_dTAG_P-P\\)\n\n# diff\ntemp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)\ncolnames(temp.tb) <- c(\\group\\, \\score\\)\nplot_insScore(temp.tb, \\nearestBoundary_diff_P-P\\, ymin = -0.5, ymax = 1)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
gene.insScore <- gene.insScore.all %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% ppOver4, \p4\,
ifelse(ensembl %in% ppOver3, \p3\,
ifelse(ensembl %in% ppOver2, \p2\,
ifelse(ensembl %in% ppOver1, \p1\,
ifelse(ensembl %in% ppOver0, \p0\, NA)))))) %>%
dplyr::filter(!is.na(group))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$score
distance2 <- (data %>% dplyr::filter(group ==group2) )$score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_insScore <- function(temp.tb, note, ymin = 0, ymax = 1.5){
p01 <- round(getPvalWilcox(temp.tb, \p0\, \p1\), 5)
p12 <- round(getPvalWilcox(temp.tb, \p1\, \p2\), 5)
p23 <- round(getPvalWilcox(temp.tb, \p2\, \p3\), 5)
p34 <- round(getPvalWilcox(temp.tb, \p3\, \p4\), 5)
p24 <- round(getPvalWilcox(temp.tb, \p2\, \p4\), 5)
p14 <- round(getPvalWilcox(temp.tb, \p1\, \p4\), 5)
p04 <- round(getPvalWilcox(temp.tb, \p0\, \p4\), 5)
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p01: \, convPvalue(p01), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p34: \, convPvalue(p34), \\n\,
\p24: \, convPvalue(p24), \\n\,
\p14: \,convPvalue( p14), \\n\,
\p04: \,convPvalue( p04), \\n\),
color = \black\, hjust = 0, size = 3) +
scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# DMSO
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_DMSO)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_DMSO_P-P\)
# dTAG
temp.tb <- gene.insScore %>% dplyr::select(group, boundaryInsScore_dTAG)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_dTAG_P-P\)
# diff
temp.tb <- gene.insScore %>% dplyr::select(group, diff_boundaryInsScore)
colnames(temp.tb) <- c(\group\, \score\)
plot_insScore(temp.tb, \nearestBoundary_diff_P-P\, ymin = -0.5, ymax = 1)
```
```
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##### Plotting for P-E
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<!-- rnb-output-begin 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-->
refDir <- here(\../..\, \reference\)
# Importing TAD boundary
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
center = (start + end)/2)
# Importing TAD
tad <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_G1DMSO.bedpe\))
colnames(tad) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\)
tad <- tad %>% dplyr::mutate(tadID = paste(chr1, start1, end1, sep = \_\),
tadSize = end1 - start1)
# Importing gene
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, V5, V1, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \gene\, \chr\, \TSS\)
# Functions
findDistanceToTAD <- function(chrom, TSS, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom) %>%
dplyr::mutate(distance = abs(center - TSS)) %>% slice_min(distance)
if(nrow(temp) == 1){
return(temp$distance)
}else{
return(NA)
}
}
findItsTAD <- function(chrom, TSS, tad){
temp <- tad %>% dplyr::filter(chr1 == chrom,
start1 < TSS,
end1 > TSS)
if(nrow(temp) == 1){
return(temp$tadID)
}else{
return(NA)
}
}
findTADSize <-function(chrom, TSS, tad){
temp <- tad %>% dplyr::filter(chr1 == chrom,
start1 < TSS,
end1 > TSS)
if(nrow(temp) == 1){
return(temp$tadSize)
}else{
return(NA)
}
}
gene.TSS.tb <- gene.TSS.tb %>% rowwise() %>%
dplyr::mutate(distance = findDistanceToTAD(chr, TSS, tad_boundary),
TAD = findItsTAD(chr, TSS, tad),
TADsize = findTADSize(chr, TSS, tad))
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
refDir <- here(\../..\, \reference\)
# Importing TAD boundary
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
center = (start + end)/2)
# Importing TAD
tad <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_G1DMSO.bedpe\))
colnames(tad) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\)
tad <- tad %>% dplyr::mutate(tadID = paste(chr1, start1, end1, sep = \_\),
tadSize = end1 - start1)
# Importing gene
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, V5, V1, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \gene\, \chr\, \TSS\)
# Functions
findDistanceToTAD <- function(chrom, TSS, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom) %>%
dplyr::mutate(distance = abs(center - TSS)) %>% slice_min(distance)
if(nrow(temp) == 1){
return(temp$distance)
}else{
return(NA)
}
}
findItsTAD <- function(chrom, TSS, tad){
temp <- tad %>% dplyr::filter(chr1 == chrom,
start1 < TSS,
end1 > TSS)
if(nrow(temp) == 1){
return(temp$tadID)
}else{
return(NA)
}
}
findTADSize <-function(chrom, TSS, tad){
temp <- tad %>% dplyr::filter(chr1 == chrom,
start1 < TSS,
end1 > TSS)
if(nrow(temp) == 1){
return(temp$tadSize)
}else{
return(NA)
}
}
gene.TSS.tb <- gene.TSS.tb %>% rowwise() %>%
dplyr::mutate(distance = findDistanceToTAD(chr, TSS, tad_boundary),
TAD = findItsTAD(chr, TSS, tad),
TADsize = findTADSize(chr, TSS, tad))
```
```
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##### Plotting for P-P
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\, NA))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## Plot distance\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n  distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = distance)) +\n    # Set axis labels (no x-axis title)\n    scale_fill_manual(values = ((c(\\#777777\\, \\#F28E2C\\)))) +\n    labs(x = NULL, y = \\Distance from TAD boundary\\) +\n    # Violin plot with black outline, customized line width and end\n    introdataviz::geom_split_violin(\n     color = \\black\\, alpha = 0.4,\n      linewidth = lineMedium * mmToLineUnit, lineend = \\square\\\n    ) +\n    # Box plot with customized line width, square end, and outlier size\n    geom_boxplot(\n      width = 0.3, color = \\black\\, alpha = 0.6,\n      linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n      outlier.shape = NA,  alpha = 0.6, show.legend = FALSE\n    ) +\n    \n    # Mean point in each group\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # Horizontal line at y = 0\n    geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n    \n    # Coordinate limits and custom y-axis labels\n    coord_cartesian(ylim = c(ymin, ymax)) +\n    scale_y_continuous(labels = label_kb_mb) +\n    \n    # Annotate p-value text\n    annotate(\n      \\text\\, x = 1, y = ymin + 1,\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) +\n    \n    # Theme customization\n    theme_classic() +\n    theme(\n      axis.title = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)) +\n    guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      )\n    )\n  \n  \n  fileName <- paste0(\\distance_to_boundary_\\, note)\n  width <- panelSize(1.55)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_TADdistance(gene.TSS.tb.plot, \\group_binayGroup\\, ymax = 1000000)\n\n\n## Plot TAD size\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize\n  distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = TADsize)) +\n    # Set axis labels (no x-axis title)\n    labs(x = NULL, y = \\Size of TAD\\) +\n        scale_fill_manual(values = ((c(\\#777777\\, \\#F28E2C\\)))) +\n\n    # Violin plot with black outline, customized line width and end\n    introdataviz::geom_split_violin(\n      color = \\black\\,\n      linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4\n    ) +\n    \n    # Box plot with customized line width, square end, and outlier size\n    geom_boxplot(\n      width = 0.3, color = \\black\\,\n      linewidth = lineMedium* mmToLineUnit, lineend = \\square\\,\n      outlier.shape = NA,  alpha = 0.6, show.legend = FALSE\n    ) +\n    \n    # Mean point in each group\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # Horizontal line at y = 0\n    geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n    \n    # Coordinate limits and custom y-axis labels\n    coord_cartesian(ylim = c(ymin, ymax)) +\n    scale_y_continuous(labels = label_kb_mb) +\n    \n    # Annotate p-value text\n    annotate(\n      \\text\\, x = 1, y = ymin + 1,\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) +\n    \n    # Theme customization\n  theme_classic() +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) + guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      ))\n  \n  \n  \n  fileName <- paste0(\\size_of_boundary_\\, note)\n  width <- panelSize(1.55)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_TADsize(gene.TSS.tb.plot, \\group_binaryGroup\\, ymax = 3e6)\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\, NA))) %>%
dplyr::filter(!is.na(group))
## Plot distance
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$distance
distance2 <- (data %>% dplyr::filter(group == group2) )$distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = \.\, fill = group, y = distance)) +
# Set axis labels (no x-axis title)
scale_fill_manual(values = ((c(\#777777\, \#F28E2C\)))) +
labs(x = NULL, y = \Distance from TAD boundary\) +
# Violin plot with black outline, customized line width and end
introdataviz::geom_split_violin(
color = \black\, alpha = 0.4,
linewidth = lineMedium * mmToLineUnit, lineend = \square\
) +
# Box plot with customized line width, square end, and outlier size
geom_boxplot(
width = 0.3, color = \black\, alpha = 0.6,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE
) +
# Mean point in each group
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
# Horizontal line at y = 0
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
# Coordinate limits and custom y-axis labels
coord_cartesian(ylim = c(ymin, ymax)) +
scale_y_continuous(labels = label_kb_mb) +
# Annotate p-value text
annotate(
\text\, x = 1, y = ymin + 1,
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
) +
# Theme customization
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)) +
guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
)
)
fileName <- paste0(\distance_to_boundary_\, note)
width <- panelSize(1.55)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADdistance(gene.TSS.tb.plot, \group_binayGroup\, ymax = 1000000)
## Plot TAD size
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize
distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = \.\, fill = group, y = TADsize)) +
# Set axis labels (no x-axis title)
labs(x = NULL, y = \Size of TAD\) +
scale_fill_manual(values = ((c(\#777777\, \#F28E2C\)))) +
# Violin plot with black outline, customized line width and end
introdataviz::geom_split_violin(
color = \black\,
linewidth = lineMedium* mmToLineUnit, lineend = \square\, alpha = .4
) +
# Box plot with customized line width, square end, and outlier size
geom_boxplot(
width = 0.3, color = \black\,
linewidth = lineMedium* mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE
) +
# Mean point in each group
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
# Horizontal line at y = 0
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
# Coordinate limits and custom y-axis labels
coord_cartesian(ylim = c(ymin, ymax)) +
scale_y_continuous(labels = label_kb_mb) +
# Annotate p-value text
annotate(
\text\, x = 1, y = ymin + 1,
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
) +
# Theme customization
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))
fileName <- paste0(\size_of_boundary_\, note)
width <- panelSize(1.55)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADsize(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 3e6)
<!-- rnb-output-end -->
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 -->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\, NA))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## Plot distance\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n  distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = distance)) +\n    # Set axis labels (no x-axis title)\n    scale_fill_manual(values = ((c(\\#777777\\, \\#F28E2C\\)))) +\n    labs(x = NULL, y = \\Distance from TAD boundary\\) +\n    # Violin plot with black outline, customized line width and end\n    introdataviz::geom_split_violin(\n     color = \\black\\, alpha = 0.4,\n      linewidth = lineMedium * mmToLineUnit, lineend = \\square\\\n    ) +\n    # Box plot with customized line width, square end, and outlier size\n    geom_boxplot(\n      width = 0.3, color = \\black\\, alpha = 0.6,\n      linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n      outlier.shape = NA,  alpha = 0.6, show.legend = FALSE\n    ) +\n    \n    # Mean point in each group\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # Horizontal line at y = 0\n    geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n    \n    # Coordinate limits and custom y-axis labels\n    coord_cartesian(ylim = c(ymin, ymax)) +\n    scale_y_continuous(labels = label_kb_mb) +\n    \n    # Annotate p-value text\n    annotate(\n      \\text\\, x = 1, y = ymin + 1,\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) +\n    \n    # Theme customization\n    theme_classic() +\n    theme(\n      axis.title = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)) +\n    guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      )\n    )\n  \n  \n  fileName <- paste0(\\distance_to_boundary_\\, note)\n  width <- panelSize(1.55)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_TADdistance(gene.TSS.tb.plot, \\group_binayGroup\\, ymax = 1000000)\n\n\n## Plot TAD size\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize\n  distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = TADsize)) +\n    # Set axis labels (no x-axis title)\n    labs(x = NULL, y = \\Size of TAD\\) +\n        scale_fill_manual(values = ((c(\\#777777\\, \\#F28E2C\\)))) +\n\n    # Violin plot with black outline, customized line width and end\n    introdataviz::geom_split_violin(\n      color = \\black\\,\n      linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4\n    ) +\n    \n    # Box plot with customized line width, square end, and outlier size\n    geom_boxplot(\n      width = 0.3, color = \\black\\,\n      linewidth = lineMedium* mmToLineUnit, lineend = \\square\\,\n      outlier.shape = NA,  alpha = 0.6, show.legend = FALSE\n    ) +\n    \n    # Mean point in each group\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # Horizontal line at y = 0\n    geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n    \n    # Coordinate limits and custom y-axis labels\n    coord_cartesian(ylim = c(ymin, ymax)) +\n    scale_y_continuous(labels = label_kb_mb) +\n    \n    # Annotate p-value text\n    annotate(\n      \\text\\, x = 1, y = ymin + 1,\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) +\n    \n    # Theme customization\n  theme_classic() +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) + guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      ))\n  \n  \n  \n  fileName <- paste0(\\size_of_boundary_\\, note)\n  width <- panelSize(1.55)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_TADsize(gene.TSS.tb.plot, \\group_binaryGroup\\, ymax = 3e6)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\, NA))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## Plot distance\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n  distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = distance)) +\n    # Set axis labels (no x-axis title)\n    scale_fill_manual(values = ((c(\\#777777\\, \\#F28E2C\\)))) +\n    labs(x = NULL, y = \\Distance from TAD boundary\\) +\n    # Violin plot with black outline, customized line width and end\n    introdataviz::geom_split_violin(\n     color = \\black\\, alpha = 0.4,\n      linewidth = lineMedium * mmToLineUnit, lineend = \\square\\\n    ) +\n    # Box plot with customized line width, square end, and outlier size\n    geom_boxplot(\n      width = 0.3, color = \\black\\, alpha = 0.6,\n      linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n      outlier.shape = NA,  alpha = 0.6, show.legend = FALSE\n    ) +\n    \n    # Mean point in each group\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # Horizontal line at y = 0\n    geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n    \n    # Coordinate limits and custom y-axis labels\n    coord_cartesian(ylim = c(ymin, ymax)) +\n    scale_y_continuous(labels = label_kb_mb) +\n    \n    # Annotate p-value text\n    annotate(\n      \\text\\, x = 1, y = ymin + 1,\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) +\n    \n    # Theme customization\n    theme_classic() +\n    theme(\n      axis.title = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)) +\n    guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      )\n    )\n  \n  \n  fileName <- paste0(\\distance_to_boundary_\\, note)\n  width <- panelSize(1.55)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_TADdistance(gene.TSS.tb.plot, \\group_binayGroup\\, ymax = 1000000)\n\n\n## Plot TAD size\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize\n  distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = TADsize)) +\n    # Set axis labels (no x-axis title)\n    labs(x = NULL, y = \\Size of TAD\\) +\n        scale_fill_manual(values = ((c(\\#777777\\, \\#F28E2C\\)))) +\n\n    # Violin plot with black outline, customized line width and end\n    introdataviz::geom_split_violin(\n      color = \\black\\,\n      linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4\n    ) +\n    \n    # Box plot with customized line width, square end, and outlier size\n    geom_boxplot(\n      width = 0.3, color = \\black\\,\n      linewidth = lineMedium* mmToLineUnit, lineend = \\square\\,\n      outlier.shape = NA,  alpha = 0.6, show.legend = FALSE\n    ) +\n    \n    # Mean point in each group\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # Horizontal line at y = 0\n    geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n    \n    # Coordinate limits and custom y-axis labels\n    coord_cartesian(ylim = c(ymin, ymax)) +\n    scale_y_continuous(labels = label_kb_mb) +\n    \n    # Annotate p-value text\n    annotate(\n      \\text\\, x = 1, y = ymin + 1,\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) +\n    \n    # Theme customization\n  theme_classic() +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) + guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      ))\n  \n  \n  \n  fileName <- paste0(\\size_of_boundary_\\, note)\n  width <- panelSize(1.55)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_TADsize(gene.TSS.tb.plot, \\group_binaryGroup\\, ymax = 3e6)\n```\n```"} -->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\, NA))) %>%
dplyr::filter(!is.na(group))
## Plot distance
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$distance
distance2 <- (data %>% dplyr::filter(group == group2) )$distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = \.\, fill = group, y = distance)) +
# Set axis labels (no x-axis title)
scale_fill_manual(values = ((c(\#777777\, \#F28E2C\)))) +
labs(x = NULL, y = \Distance from TAD boundary\) +
# Violin plot with black outline, customized line width and end
introdataviz::geom_split_violin(
color = \black\, alpha = 0.4,
linewidth = lineMedium * mmToLineUnit, lineend = \square\
) +
# Box plot with customized line width, square end, and outlier size
geom_boxplot(
width = 0.3, color = \black\, alpha = 0.6,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE
) +
# Mean point in each group
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
# Horizontal line at y = 0
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
# Coordinate limits and custom y-axis labels
coord_cartesian(ylim = c(ymin, ymax)) +
scale_y_continuous(labels = label_kb_mb) +
# Annotate p-value text
annotate(
\text\, x = 1, y = ymin + 1,
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
) +
# Theme customization
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)) +
guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
)
)
fileName <- paste0(\distance_to_boundary_\, note)
width <- panelSize(1.55)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADdistance(gene.TSS.tb.plot, \group_binayGroup\, ymax = 1000000)
## Plot TAD size
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize
distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = \.\, fill = group, y = TADsize)) +
# Set axis labels (no x-axis title)
labs(x = NULL, y = \Size of TAD\) +
scale_fill_manual(values = ((c(\#777777\, \#F28E2C\)))) +
# Violin plot with black outline, customized line width and end
introdataviz::geom_split_violin(
color = \black\,
linewidth = lineMedium* mmToLineUnit, lineend = \square\, alpha = .4
) +
# Box plot with customized line width, square end, and outlier size
geom_boxplot(
width = 0.3, color = \black\,
linewidth = lineMedium* mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE
) +
# Mean point in each group
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
# Horizontal line at y = 0
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
# Coordinate limits and custom y-axis labels
coord_cartesian(ylim = c(ymin, ymax)) +
scale_y_continuous(labels = label_kb_mb) +
# Annotate p-value text
annotate(
\text\, x = 1, y = ymin + 1,
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
) +
# Theme customization
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))
fileName <- paste0(\size_of_boundary_\, note)
width <- panelSize(1.55)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADsize(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 3e6)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.15] TAD distance/size
#### Link TAD
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\,\n                        ifelse(ensembl %in% group5, \\group5\\,\n                               ifelse(ensembl %in% group8, \\group8\\, NA))))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## Plot distance\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n  distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = distance)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n    annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\distance_to_boundary_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_TADdistance(gene.TSS.tb.plot, \\group\\)\n\n\n## Plot TAD size\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize\n  distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = TADsize)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n    annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\size_of_boundary_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_TADsize(gene.TSS.tb.plot, \\group\\, ymax = 6e6)\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
## Plot distance
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$distance
distance2 <- (data %>% dplyr::filter(group == group2) )$distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + scale_y_continuous(labels = label_kb_mb) +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\distance_to_boundary_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADdistance(gene.TSS.tb.plot, \group\)
## Plot TAD size
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize
distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = TADsize)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + scale_y_continuous(labels = label_kb_mb) +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\size_of_boundary_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADsize(gene.TSS.tb.plot, \group\, ymax = 6e6)
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\,\n                        ifelse(ensembl %in% group5, \\group5\\,\n                               ifelse(ensembl %in% group8, \\group8\\, NA))))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## Plot distance\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n  distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = distance)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n    annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\distance_to_boundary_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_TADdistance(gene.TSS.tb.plot, \\group\\)\n\n\n## Plot TAD size\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize\n  distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = TADsize)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n    annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\size_of_boundary_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_TADsize(gene.TSS.tb.plot, \\group\\, ymax = 6e6)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
## Plot distance
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$distance
distance2 <- (data %>% dplyr::filter(group == group2) )$distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + scale_y_continuous(labels = label_kb_mb) +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\distance_to_boundary_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADdistance(gene.TSS.tb.plot, \group\)
## Plot TAD size
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize
distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = TADsize)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + scale_y_continuous(labels = label_kb_mb) +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\size_of_boundary_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADsize(gene.TSS.tb.plot, \group\, ymax = 6e6)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### dTAG - binaryGroup
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup3 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group3.tsv\\))$gene\ngroup4 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group4.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup6 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group6.tsv\\))$gene\ngroup7 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group7.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group8.tsv\\))$gene\ngroup9 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group9.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>%\n  rowwise() %>%\n  dplyr::mutate(\n    group = dplyr::case_when(\n      ensembl %in% group1 ~ \\group1\\,\n      ensembl %in% group2 ~ \\group2\\,\n      ensembl %in% group3 ~ \\group3\\,\n      ensembl %in% group4 ~ \\group4\\,\n      ensembl %in% group5 ~ \\group5\\,\n      ensembl %in% group6 ~ \\group6\\,\n      ensembl %in% group7 ~ \\group7\\,\n      ensembl %in% group8 ~ \\group8\\,\n      ensembl %in% group9 ~ \\group9\\,\n      TRUE ~ NA_character_\n    )\n  ) %>%\n  dplyr::filter(!is.na(group))\n\n## Plot distance\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n#   distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n\n\nplot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  # p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  # p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  # p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  # p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  # p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  # p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  # \n  p <- ggplot(temp.tb, aes(x = group, y = distance)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + scale_y_continuous(labels = label_kb_mb)\n    # annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n    #                                                      \\p15: \\, convPvalue(p15), \\\\n\\,\n    #                                                      \\p18: \\, convPvalue(p18), \\\\n\\,\n    #                                                      \\p25: \\, convPvalue(p25), \\\\n\\,\n    #                                                      \\p28: \\, convPvalue(p28), \\\\n\\,\n    #                                                      \\p58: \\,convPvalue( p58), \\\\n\\),\n    #          color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\distance_to_boundary_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_TADdistance(gene.TSS.tb.plot, \\group_A485\\)\n\n\n## Plot TAD size\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize\n#   distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# \n\nplot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  # p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  # p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  # p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  # p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  # p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  # p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = TADsize)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + scale_y_continuous(labels = label_kb_mb) \n    # annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n    #                                                      \\p15: \\, convPvalue(p15), \\\\n\\,\n    #                                                      \\p18: \\, convPvalue(p18), \\\\n\\,\n    #                                                      \\p25: \\, convPvalue(p25), \\\\n\\,\n    #                                                      \\p28: \\, convPvalue(p28), \\\\n\\,\n    #                                                      \\p58: \\,convPvalue( p58), \\\\n\\),\n    #          color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\size_of_boundary_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_TADsize(gene.TSS.tb.plot, \\group_A485\\, ymax = 6e6)\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group2.tsv\))$gene
group3 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group3.tsv\))$gene
group4 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group4.tsv\))$gene
group5 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group5.tsv\))$gene
group6 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group6.tsv\))$gene
group7 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group7.tsv\))$gene
group8 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group8.tsv\))$gene
group9 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group9.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>%
rowwise() %>%
dplyr::mutate(
group = dplyr::case_when(
ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
ensembl %in% group3 ~ \group3\,
ensembl %in% group4 ~ \group4\,
ensembl %in% group5 ~ \group5\,
ensembl %in% group6 ~ \group6\,
ensembl %in% group7 ~ \group7\,
ensembl %in% group8 ~ \group8\,
ensembl %in% group9 ~ \group9\,
TRUE ~ NA_character_
)
) %>%
dplyr::filter(!is.na(group))
## Plot distance
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group == group1) )$distance
# distance2 <- (data %>% dplyr::filter(group == group2) )$distance
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
plot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){
# p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
# p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
# p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
# p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
# p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
# p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
#
p <- ggplot(temp.tb, aes(x = group, y = distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + scale_y_continuous(labels = label_kb_mb)
# annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
# \p15: \, convPvalue(p15), \\n\,
# \p18: \, convPvalue(p18), \\n\,
# \p25: \, convPvalue(p25), \\n\,
# \p28: \, convPvalue(p28), \\n\,
# \p58: \,convPvalue( p58), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\distance_to_boundary_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADdistance(gene.TSS.tb.plot, \group_A485\)
## Plot TAD size
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize
# distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
plot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){
# p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
# p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
# p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
# p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
# p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
# p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = TADsize)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + scale_y_continuous(labels = label_kb_mb)
# annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
# \p15: \, convPvalue(p15), \\n\,
# \p18: \, convPvalue(p18), \\n\,
# \p25: \, convPvalue(p25), \\n\,
# \p28: \, convPvalue(p28), \\n\,
# \p58: \,convPvalue( p58), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\size_of_boundary_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADsize(gene.TSS.tb.plot, \group_A485\, ymax = 6e6)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup3 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group3.tsv\\))$gene\ngroup4 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group4.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup6 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group6.tsv\\))$gene\ngroup7 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group7.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group8.tsv\\))$gene\ngroup9 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group9.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>%\n  rowwise() %>%\n  dplyr::mutate(\n    group = dplyr::case_when(\n      ensembl %in% group1 ~ \\group1\\,\n      ensembl %in% group2 ~ \\group2\\,\n      ensembl %in% group3 ~ \\group3\\,\n      ensembl %in% group4 ~ \\group4\\,\n      ensembl %in% group5 ~ \\group5\\,\n      ensembl %in% group6 ~ \\group6\\,\n      ensembl %in% group7 ~ \\group7\\,\n      ensembl %in% group8 ~ \\group8\\,\n      ensembl %in% group9 ~ \\group9\\,\n      TRUE ~ NA_character_\n    )\n  ) %>%\n  dplyr::filter(!is.na(group))\n\n## Plot distance\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n#   distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n\n\nplot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  # p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  # p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  # p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  # p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  # p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  # p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  # \n  p <- ggplot(temp.tb, aes(x = group, y = distance)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + scale_y_continuous(labels = label_kb_mb)\n    # annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n    #                                                      \\p15: \\, convPvalue(p15), \\\\n\\,\n    #                                                      \\p18: \\, convPvalue(p18), \\\\n\\,\n    #                                                      \\p25: \\, convPvalue(p25), \\\\n\\,\n    #                                                      \\p28: \\, convPvalue(p28), \\\\n\\,\n    #                                                      \\p58: \\,convPvalue( p58), \\\\n\\),\n    #          color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\distance_to_boundary_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_TADdistance(gene.TSS.tb.plot, \\group_A485\\)\n\n\n## Plot TAD size\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize\n#   distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# \n\nplot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  # p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  # p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  # p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  # p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  # p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  # p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = TADsize)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + scale_y_continuous(labels = label_kb_mb) \n    # annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n    #                                                      \\p15: \\, convPvalue(p15), \\\\n\\,\n    #                                                      \\p18: \\, convPvalue(p18), \\\\n\\,\n    #                                                      \\p25: \\, convPvalue(p25), \\\\n\\,\n    #                                                      \\p28: \\, convPvalue(p28), \\\\n\\,\n    #                                                      \\p58: \\,convPvalue( p58), \\\\n\\),\n    #          color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\size_of_boundary_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_TADsize(gene.TSS.tb.plot, \\group_A485\\, ymax = 6e6)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
group1 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group2.tsv\))$gene
group3 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group3.tsv\))$gene
group4 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group4.tsv\))$gene
group5 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group5.tsv\))$gene
group6 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group6.tsv\))$gene
group7 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group7.tsv\))$gene
group8 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group8.tsv\))$gene
group9 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group9.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>%
rowwise() %>%
dplyr::mutate(
group = dplyr::case_when(
ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
ensembl %in% group3 ~ \group3\,
ensembl %in% group4 ~ \group4\,
ensembl %in% group5 ~ \group5\,
ensembl %in% group6 ~ \group6\,
ensembl %in% group7 ~ \group7\,
ensembl %in% group8 ~ \group8\,
ensembl %in% group9 ~ \group9\,
TRUE ~ NA_character_
)
) %>%
dplyr::filter(!is.na(group))
## Plot distance
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group == group1) )$distance
# distance2 <- (data %>% dplyr::filter(group == group2) )$distance
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
plot_TADdistance <- function(temp.tb, note, ymin = 0, ymax = 2000000){
# p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
# p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
# p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
# p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
# p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
# p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
#
p <- ggplot(temp.tb, aes(x = group, y = distance)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + scale_y_continuous(labels = label_kb_mb)
# annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
# \p15: \, convPvalue(p15), \\n\,
# \p18: \, convPvalue(p18), \\n\,
# \p25: \, convPvalue(p25), \\n\,
# \p28: \, convPvalue(p28), \\n\,
# \p58: \,convPvalue( p58), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\distance_to_boundary_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADdistance(gene.TSS.tb.plot, \group_A485\)
## Plot TAD size
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group == group1) )$TADsize
# distance2 <- (data %>% dplyr::filter(group == group2) )$TADsize
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
#
plot_TADsize <- function(temp.tb, note, ymin = 0, ymax = 2000000){
# p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
# p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
# p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
# p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
# p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
# p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = TADsize)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + scale_y_continuous(labels = label_kb_mb)
# annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
# \p15: \, convPvalue(p15), \\n\,
# \p18: \, convPvalue(p18), \\n\,
# \p25: \, convPvalue(p25), \\n\,
# \p28: \, convPvalue(p28), \\n\,
# \p58: \,convPvalue( p58), \\n\),
# color = \black\, hjust = 0, size = 3)
fileName <- paste0(\size_of_boundary_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_TADsize(gene.TSS.tb.plot, \group_A485\, ymax = 6e6)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### dTAG - Plotting for groups
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refDir <- here(\../..\, \reference\)
# Importing TAD boundary
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
start = start -175000/2,
end = end + 175000/2,
center = (start + end)/2)
# Importing RAD21 bigwig track
bw.RAD21 <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm_bin50bp.bw\))
# Create GRanges object for TAD boundaries
tad_ranges <- makeGRangesFromDataFrame(tad_boundary, keep.extra.columns = TRUE)
# Find overlaps between all TAD boundaries and RAD21 bigwig data
overlaps <- findOverlaps(tad_ranges, bw.RAD21)
# Extract overlapping regions and scores from the BigWig
overlapping_bw <- bw.RAD21[subjectHits(overlaps)]
overlapping_tads <- tad_ranges[queryHits(overlaps)]
# Aggregate scores by TAD boundary regions
scores <- as_tibble(overlapping_bw) %>%
dplyr::mutate(tad_id = tad_ranges[queryHits(overlaps)]$tad_id) %>%
group_by(tad_id) %>%
summarise(rad21Score = sum(score))
tad_boundary <- tad_boundary %>%
left_join(scores, by = \tad_id\)
# Importing gene
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, V5, V1, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \gene\, \chr\, \TSS\)
# Find nearest TAD boundary
findClosestTADBoundaryID <- function(chrom, TSS, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom) %>%
dplyr::mutate(distance = abs(center - TSS)) %>%
slice_min(distance)
if(nrow(temp) == 1){
return(temp$tad_id)
}else{
return(NA)
}
}
gene.TSS.tb <- gene.TSS.tb %>% rowwise() %>%
dplyr::mutate(closestBoundary = findClosestTADBoundaryID(chr, TSS, tad_boundary))
temp <- tad_boundary %>% dplyr::select(tad_id, rad21Score)
gene.TSS.tb <- gene.TSS.tb %>% left_join(temp, by = c(\closestBoundary\ = \tad_id\) )
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```r
```r
refDir <- here(\../..\, \reference\)
# Importing TAD boundary
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
start = start -175000/2,
end = end + 175000/2,
center = (start + end)/2)
# Importing RAD21 bigwig track
bw.RAD21 <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm_bin50bp.bw\))
# Create GRanges object for TAD boundaries
tad_ranges <- makeGRangesFromDataFrame(tad_boundary, keep.extra.columns = TRUE)
# Find overlaps between all TAD boundaries and RAD21 bigwig data
overlaps <- findOverlaps(tad_ranges, bw.RAD21)
# Extract overlapping regions and scores from the BigWig
overlapping_bw <- bw.RAD21[subjectHits(overlaps)]
overlapping_tads <- tad_ranges[queryHits(overlaps)]
# Aggregate scores by TAD boundary regions
scores <- as_tibble(overlapping_bw) %>%
dplyr::mutate(tad_id = tad_ranges[queryHits(overlaps)]$tad_id) %>%
group_by(tad_id) %>%
summarise(rad21Score = sum(score))
tad_boundary <- tad_boundary %>%
left_join(scores, by = \tad_id\)
# Importing gene
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, V5, V1, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \gene\, \chr\, \TSS\)
# Find nearest TAD boundary
findClosestTADBoundaryID <- function(chrom, TSS, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom) %>%
dplyr::mutate(distance = abs(center - TSS)) %>%
slice_min(distance)
if(nrow(temp) == 1){
return(temp$tad_id)
}else{
return(NA)
}
}
gene.TSS.tb <- gene.TSS.tb %>% rowwise() %>%
dplyr::mutate(closestBoundary = findClosestTADBoundaryID(chr, TSS, tad_boundary))
temp <- tad_boundary %>% dplyr::select(tad_id, rad21Score)
gene.TSS.tb <- gene.TSS.tb %>% left_join(temp, by = c(\closestBoundary\ = \tad_id\) )
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
##### A485 - Plotting for groups
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\,\n                        ifelse(ensembl %in% group5, \\group5\\,\n                               ifelse(ensembl %in% group8, \\group8\\, NA))))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## Plot distance\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$rad21Score\n  distance2 <- (data %>% dplyr::filter(group == group2) )$rad21Score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_rad21ScoreAtBoundary <- function(temp.tb, note){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = rad21Score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + \n    annotate(\\text\\, x = 1, y = 20 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\rad21Score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_rad21ScoreAtBoundary(gene.TSS.tb.plot, \\group\\)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
## Plot distance
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$rad21Score
distance2 <- (data %>% dplyr::filter(group == group2) )$rad21Score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_rad21ScoreAtBoundary <- function(temp.tb, note){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = rad21Score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 20 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\rad21Score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_rad21ScoreAtBoundary(gene.TSS.tb.plot, \group\)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\,\n                        ifelse(ensembl %in% group5, \\group5\\,\n                               ifelse(ensembl %in% group8, \\group8\\, NA))))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## Plot distance\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$rad21Score\n  distance2 <- (data %>% dplyr::filter(group == group2) )$rad21Score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_rad21ScoreAtBoundary <- function(temp.tb, note){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = rad21Score)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() + \n    annotate(\\text\\, x = 1, y = 20 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\rad21Score_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\nplot_rad21ScoreAtBoundary(gene.TSS.tb.plot, \\group\\)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
## Plot distance
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$rad21Score
distance2 <- (data %>% dplyr::filter(group == group2) )$rad21Score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_rad21ScoreAtBoundary <- function(temp.tb, note){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = rad21Score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 20 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\rad21Score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_rad21ScoreAtBoundary(gene.TSS.tb.plot, \group\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.16] Checking the intensity of RAD21 at TAD boundary
#### Calculating RAD21 intensity of the nearest TAD boundary
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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### IMPORTING REQUIRED DATA
# Importing TAD
tad <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_G1DMSO.bedpe\))
colnames(tad) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\)
tad <- tad %>% dplyr::mutate(tadID = paste(chr1, start1, end1, sep = \_\))
# Importing gene
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, V5, V1, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \gene\, \chr\, \TSS\)
# Importing enhancer
peak.H3K27ac <- as_tibble(importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))) %>%
dplyr::mutate(center = (start + end)/2)
### FUNCTION FOR COUNTING
findItsTAD <- function(chrom, coordinate, tad){
temp <- tad %>% dplyr::filter(chr1 == chrom,
start1 < coordinate,
end1 > coordinate)
if(nrow(temp) == 1){
return(temp$tadID)
}else{
return(NA)
}
}
### Counting each features for TAD
### Counting gene
geneCountPerTAD <- gene.TSS.tb %>% rowwise() %>%
dplyr::mutate(TAD = findItsTAD(chr, TSS, tad)) %>%
drop_na() %>%
group_by(TAD) %>%
summarize(count = n())
### Counting enhancer
enhCountPerTAD <- peak.H3K27ac %>% rowwise() %>%
dplyr::mutate(TAD = findItsTAD(seqnames, center, tad)) %>%
drop_na() %>%
group_by(TAD) %>%
summarize(count = n())
### Get TAD sizez
tad.db <- tad %>% left_join(geneCountPerTAD, by = c(\tadID\ = \TAD\), ) %>%
left_join(enhCountPerTAD, by = c(\tadID\ = \TAD\)) %>%
dplyr::select(c(1, 2, 3, 7, 8, 9)) %>%
mutate_all(~replace(., is.na(.), 0))
colnames(tad.db) <- c(\chr\, \start\, \end\, \tadID\, \geneCount\, \enhCount\)
tad.db <- tad.db %>% dplyr::mutate(
tadSize = end - start,
geneDensity = geneCount/tadSize * 100e3,
enhDensity = enhCount/tadSize* 100e3,
regDensity = (geneCount + enhCount)/tadSize* 100e3
)
### Assign TAD and information to gene
gene.TSS.tb <- gene.TSS.tb %>% rowwise() %>%
dplyr::mutate(TAD = findItsTAD(chr, TSS, tad))
#gene.TSS.tb <- gene.TSS.tb %>% dplyr::filter(!is.na(TAD))
temp <- tad.db %>% dplyr::select(-c(1, 2, 3))
gene.TSS.tb <- gene.TSS.tb %>% dplyr::left_join(temp, by = c(\TAD\ = \tadID\))
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-->
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```r
```r
### IMPORTING REQUIRED DATA
# Importing TAD
tad <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_G1DMSO.bedpe\))
colnames(tad) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\)
tad <- tad %>% dplyr::mutate(tadID = paste(chr1, start1, end1, sep = \_\))
# Importing gene
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, V5, V1, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \gene\, \chr\, \TSS\)
# Importing enhancer
peak.H3K27ac <- as_tibble(importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))) %>%
dplyr::mutate(center = (start + end)/2)
### FUNCTION FOR COUNTING
findItsTAD <- function(chrom, coordinate, tad){
temp <- tad %>% dplyr::filter(chr1 == chrom,
start1 < coordinate,
end1 > coordinate)
if(nrow(temp) == 1){
return(temp$tadID)
}else{
return(NA)
}
}
### Counting each features for TAD
### Counting gene
geneCountPerTAD <- gene.TSS.tb %>% rowwise() %>%
dplyr::mutate(TAD = findItsTAD(chr, TSS, tad)) %>%
drop_na() %>%
group_by(TAD) %>%
summarize(count = n())
### Counting enhancer
enhCountPerTAD <- peak.H3K27ac %>% rowwise() %>%
dplyr::mutate(TAD = findItsTAD(seqnames, center, tad)) %>%
drop_na() %>%
group_by(TAD) %>%
summarize(count = n())
### Get TAD sizez
tad.db <- tad %>% left_join(geneCountPerTAD, by = c(\tadID\ = \TAD\), ) %>%
left_join(enhCountPerTAD, by = c(\tadID\ = \TAD\)) %>%
dplyr::select(c(1, 2, 3, 7, 8, 9)) %>%
mutate_all(~replace(., is.na(.), 0))
colnames(tad.db) <- c(\chr\, \start\, \end\, \tadID\, \geneCount\, \enhCount\)
tad.db <- tad.db %>% dplyr::mutate(
tadSize = end - start,
geneDensity = geneCount/tadSize * 100e3,
enhDensity = enhCount/tadSize* 100e3,
regDensity = (geneCount + enhCount)/tadSize* 100e3
)
### Assign TAD and information to gene
gene.TSS.tb <- gene.TSS.tb %>% rowwise() %>%
dplyr::mutate(TAD = findItsTAD(chr, TSS, tad))
#gene.TSS.tb <- gene.TSS.tb %>% dplyr::filter(!is.na(TAD))
temp <- tad.db %>% dplyr::select(-c(1, 2, 3))
gene.TSS.tb <- gene.TSS.tb %>% dplyr::left_join(temp, by = c(\TAD\ = \tadID\))
```
```
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##### Plotting for groups
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<!-- rnb-output-begin 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-->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\, NA))) %>%
dplyr::filter(!is.na(group))
## geneCount
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$geneCount
distance2 <- (data %>% dplyr::filter(group == group2) )$geneCount
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_geneCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = group, y = geneCount)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\geneCount_\, note)
height <- 3
width <- 1.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_geneCount(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 200)
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```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\, NA))) %>%
dplyr::filter(!is.na(group))
## geneCount
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$geneCount
distance2 <- (data %>% dplyr::filter(group == group2) )$geneCount
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_geneCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = group, y = geneCount)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\geneCount_\, note)
height <- 3
width <- 1.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_geneCount(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 200)
```
```
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## [2.17] Checking the density of gene / enhancer within designated TAD
#### Making TAD gene count
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\,\n                        ifelse(ensembl %in% group5, \\group5\\,\n                               ifelse(ensembl %in% group8, \\group8\\, NA))))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## geneCount\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$geneCount\n  distance2 <- (data %>% dplyr::filter(group == group2) )$geneCount\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_geneCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = geneCount)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() +\n    annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\geneCount_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_geneCount(gene.TSS.tb.plot, \\group\\, ymax = 200)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
## geneCount
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$geneCount
distance2 <- (data %>% dplyr::filter(group == group2) )$geneCount
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_geneCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = geneCount)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\geneCount_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_geneCount(gene.TSS.tb.plot, \group\, ymax = 200)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\ngene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(\n  group = ifelse(ensembl %in% group1, \\group1\\,\n                 ifelse(ensembl %in% group2, \\group2\\,\n                        ifelse(ensembl %in% group5, \\group5\\,\n                               ifelse(ensembl %in% group8, \\group8\\, NA))))) %>% \n  dplyr::filter(!is.na(group))\n\n\n## geneCount\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$geneCount\n  distance2 <- (data %>% dplyr::filter(group == group2) )$geneCount\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_geneCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  p15 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group5\\), 5)\n  p18 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group8\\), 5)\n  p25 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group5\\), 5)\n  p28 <- round(getPvalWilcox(temp.tb, \\group2\\, \\group8\\), 5)\n  p58 <- round(getPvalWilcox(temp.tb, \\group5\\, \\group8\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = group, y = geneCount)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n    theme_classic() +\n    annotate(\\text\\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                                                         \\p15: \\, convPvalue(p15), \\\\n\\,\n                                                         \\p18: \\, convPvalue(p18), \\\\n\\,\n                                                         \\p25: \\, convPvalue(p25), \\\\n\\,\n                                                         \\p28: \\, convPvalue(p28), \\\\n\\,\n                                                         \\p58: \\,convPvalue( p58), \\\\n\\),\n             color = \\black\\, hjust = 0, size = 3)\n  \n  \n  fileName <- paste0(\\geneCount_\\, note)\n  height <- 3\n  width <- 3\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_geneCount(gene.TSS.tb.plot, \\group\\, ymax = 200)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
gene.TSS.tb.plot <- gene.TSS.tb %>% rowwise() %>% dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group))
## geneCount
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$geneCount
distance2 <- (data %>% dplyr::filter(group == group2) )$geneCount
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_geneCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = geneCount)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\geneCount_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_geneCount(gene.TSS.tb.plot, \group\, ymax = 200)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Plotting for groups
##### geneCount
###### Binary group
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVJeU1nWlc1b1EyOTFiblJjYm1kbGRGQjJZV3hYYVd4amIzZ2dQQzBnWm5WdVkzUnBiMjRvWkdGMFlTd2daM0p2ZFhBeExDQm5jbTkxY0RJcGUxeHVJQ0JrYVhOMFlXNWpaVEVnUEMwZ0tHUmhkR0VnSlQ0bElHUndiSGx5T2pwbWFXeDBaWElvWjNKdmRYQWdQVDBnWjNKdmRYQXhLU0FwSkdWdWFFTnZkVzUwWEc0Z0lHUnBjM1JoYm1ObE1pQThMU0FvWkdGMFlTQWxQaVVnWkhCc2VYSTZPbVpwYkhSbGNpaG5jbTkxY0NBOVBTQm5jbTkxY0RJcElDa2taVzVvUTI5MWJuUmNiaUFnZDJsc0lEd3RJSGRwYkdOdmVDNTBaWE4wS0dScGMzUmhibU5sTVN3Z1pHbHpkR0Z1WTJVeUtWeHVJQ0J5WlhSMWNtNG9kMmxzSkhBdWRtRnNkV1VwWEc1OVhHNWNibHh1Y0d4dmRGOWxibWhEYjNWdWRDQThMU0JtZFc1amRHbHZiaWgwWlcxd0xuUmlMQ0J1YjNSbExDQjViV2x1SUQwZ01Dd2dlVzFoZUNBOUlESXdNREF3TURBcGUxeHVJQ0J3TVRJZ1BDMGdjbTkxYm1Rb1oyVjBVSFpoYkZkcGJHTnZlQ2gwWlcxd0xuUmlMQ0JjSW1keWIzVndNVndpTENCY0ltZHliM1Z3TWx3aUtTd2dOU2xjYmlBZ1hHNGdJSEFnUEMwZ1oyZHdiRzkwS0hSbGJYQXVkR0lzSUdGbGN5aDRJRDBnWjNKdmRYQXNJSGtnUFNCbGJtaERiM1Z1ZENrcElDc2daMlZ2YlY5MmFXOXNhVzRvWVdWektHWnBiR3dnUFNCbmNtOTFjQ2tzSUhOb2IzY3ViR1ZuWlc1a0lEMGdSa0ZNVTBVcElDc2dYRzRnSUNBZ1oyVnZiVjlpYjNod2JHOTBLSGRwWkhSb0lEMGdNQzR4TENCdmRYUnNhV1Z5TG5Ob1lYQmxJRDBnVGtFcElDdGNiaUFnSUNCemRHRjBYM04xYlcxaGNua29ZV1Z6S0dkeWIzVndJRDBnWjNKdmRYQXBMQ0JtZFc0Z1BTQnRaV0Z1TENCblpXOXRJRDBnWENKd2IybHVkRndpTENCemFHRndaU0E5SURJeExDQnphWHBsSUQwZ01pd2dabWxzYkNBOUlGd2ljbVZrWENJc0lHTnZiRzl5SUQwZ1hDSmliR0ZqYTF3aUtTQXJYRzRnSUNBZ2RHaGxiV1ZmWTJ4aGMzTnBZeWdwSUN0Y2JpQWdJQ0JoYm01dmRHRjBaU2hjSW5SbGVIUmNJaXdnZUNBOUlERXNJSGtnUFNBb2VXMXBiaUFySUhsdFlYZ3BMeklnS3lBeExDQnNZV0psYkNBOUlIQmhjM1JsTUNoY0luQXhNam9nWENJc0lHTnZiblpRZG1Gc2RXVW9jREV5S1Nrc1hHNGdJQ0FnSUNBZ0lDQWdJQ0FnWTI5c2IzSWdQU0JjSW1Kc1lXTnJYQ0lzSUdocWRYTjBJRDBnTUN3Z2MybDZaU0E5SURNcFhHNGdJRnh1SUNCY2JpQWdabWxzWlU1aGJXVWdQQzBnY0dGemRHVXdLRndpWlc1b1EyOTFiblJmWENJc0lHNXZkR1VwWEc0Z0lHaGxhV2RvZENBOExTQXpYRzRnSUhkcFpIUm9JRHd0SURFdU5WeHVJQ0J3Ym1jb2FHVnlaU2htYVdkRWFYSXNJSEJoYzNSbE1DaG1hV3hsVG1GdFpTd2dYQ0l1Y0c1blhDSXBLU3dnY21WeklEMGdOakF3TENCMWJtbDBJRDBnWENKcGJsd2lMQ0JvWldsbmFIUWdQU0JvWldsbmFIUXNJSGRwWkhSb0lEMGdkMmxrZEdncFhHNGdJSEJ5YVc1MEtIQXBYRzRnSUdSbGRpNXZabVlvS1Z4dUlDQnpkbWRzYVhSbEtHaGxjbVVvWm1sblJHbHlMQ0J3WVhOMFpUQW9abWxzWlU1aGJXVXNJRndpTG5OMloxd2lLU2tzSUNCb1pXbG5hSFFnUFNCb1pXbG5hSFFzSUhkcFpIUm9JRDBnZDJsa2RHZ3BYRzRnSUhCeWFXNTBLSEFwWEc0Z0lHUmxkaTV2Wm1Zb0tWeHVmVnh1Y0d4dmRGOWxibWhEYjNWdWRDaG5aVzVsTGxSVFV5NTBZaTV3Ykc5MExDQmNJbWR5YjNWd1gySnBibUZ5ZVVkeWIzVndYQ0lzSUhsdFlYZ2dQU0F5TURBcFhHNWNibUJnWUNKOSAtLT5cblxuYGBgclxuIyMgZW5oQ291bnRcbmdldFB2YWxXaWxjb3ggPC0gZnVuY3Rpb24oZGF0YSwgZ3JvdXAxLCBncm91cDIpe1xuICBkaXN0YW5jZTEgPC0gKGRhdGEgJT4lIGRwbHlyOjpmaWx0ZXIoZ3JvdXAgPT0gZ3JvdXAxKSApJGVuaENvdW50XG4gIGRpc3RhbmNlMiA8LSAoZGF0YSAlPiUgZHBseXI6OmZpbHRlcihncm91cCA9PSBncm91cDIpICkkZW5oQ291bnRcbiAgd2lsIDwtIHdpbGNveC50ZXN0KGRpc3RhbmNlMSwgZGlzdGFuY2UyKVxuICByZXR1cm4od2lsJHAudmFsdWUpXG59XG5cblxucGxvdF9lbmhDb3VudCA8LSBmdW5jdGlvbih0ZW1wLnRiLCBub3RlLCB5bWluID0gMCwgeW1heCA9IDIwMDAwMDApe1xuICBwMTIgPC0gcm91bmQoZ2V0UHZhbFdpbGNveCh0ZW1wLnRiLCBcXGdyb3VwMVxcLCBcXGdyb3VwMlxcKSwgNSlcbiAgXG4gIHAgPC0gZ2dwbG90KHRlbXAudGIsIGFlcyh4ID0gZ3JvdXAsIHkgPSBlbmhDb3VudCkpICsgZ2VvbV92aW9saW4oYWVzKGZpbGwgPSBncm91cCksIHNob3cubGVnZW5kID0gRkFMU0UpICsgXG4gICAgZ2VvbV9ib3hwbG90KHdpZHRoID0gMC4xLCBvdXRsaWVyLnNoYXBlID0gTkEpICtcbiAgICBzdGF0X3N1bW1hcnkoYWVzKGdyb3VwID0gZ3JvdXApLCBmdW4gPSBtZWFuLCBnZW9tID0gXFxwb2ludFxcLCBzaGFwZSA9IDIxLCBzaXplID0gMiwgZmlsbCA9IFxccmVkXFwsIGNvbG9yID0gXFxibGFja1xcKSArXG4gICAgdGhlbWVfY2xhc3NpYygpICtcbiAgICBhbm5vdGF0ZShcXHRleHRcXCwgeCA9IDEsIHkgPSAoeW1pbiArIHltYXgpLzIgKyAxLCBsYWJlbCA9IHBhc3RlMChcXHAxMjogXFwsIGNvbnZQdmFsdWUocDEyKSksXG4gICAgICAgICAgICAgY29sb3IgPSBcXGJsYWNrXFwsIGhqdXN0ID0gMCwgc2l6ZSA9IDMpXG4gIFxuICBcbiAgZmlsZU5hbWUgPC0gcGFzdGUwKFxcZW5oQ291bnRfXFwsIG5vdGUpXG4gIGhlaWdodCA8LSAzXG4gIHdpZHRoIDwtIDEuNVxuICBwbmcoaGVyZShmaWdEaXIsIHBhc3RlMChmaWxlTmFtZSwgXFwucG5nXFwpKSwgcmVzID0gNjAwLCB1bml0ID0gXFxpblxcLCBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG4gIHByaW50KHApXG4gIGRldi5vZmYoKVxuICBzdmdsaXRlKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnN2Z1xcKSksICBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG4gIHByaW50KHApXG4gIGRldi5vZmYoKVxufVxucGxvdF9lbmhDb3VudChnZW5lLlRTUy50Yi5wbG90LCBcXGdyb3VwX2JpbmFyeUdyb3VwXFwsIHltYXggPSAyMDApXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
## enhCount
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$enhCount
distance2 <- (data %>% dplyr::filter(group == group2) )$enhCount
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_enhCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = group, y = enhCount)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\enhCount_\, note)
height <- 3
width <- 1.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_enhCount(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 200)
<!-- rnb-output-end -->
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```r
```r
## enhCount
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$enhCount
distance2 <- (data %>% dplyr::filter(group == group2) )$enhCount
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_enhCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = group, y = enhCount)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\enhCount_\, note)
height <- 3
width <- 1.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_enhCount(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 200)
```
```
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###### Group
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## enhCount
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$enhCount
distance2 <- (data %>% dplyr::filter(group == group2) )$enhCount
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_enhCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = enhCount)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\enhCount_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_enhCount(gene.TSS.tb.plot, \group\, ymax = 200)
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-->
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```r
```r
## enhCount
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$enhCount
distance2 <- (data %>% dplyr::filter(group == group2) )$enhCount
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_enhCount <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = enhCount)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\enhCount_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_enhCount(gene.TSS.tb.plot, \group\, ymax = 200)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
##### enhCount
###### Binary Group
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Set axis labels (no x-axis title)\n    labs(x = NULL, y = \\Gene density within TAD\\) +\n    scale_fill_manual(values = (rev(c(\\#777777\\,\\#F28E2C\\)))) +\n    # Violin plot with black outline, customized line width and end\n    introdataviz::geom_split_violin(linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                                    alpha = .4\n    ) +\n    \n    # Box plot with customized line width, square end, and outlier size\n    geom_boxplot(\n      width = 0.3, color = \\black\\,\n      linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n      outlier.shape = NA,  alpha = 0.6, show.legend = FALSE\n    ) +\n    \n    # Mean point in each group\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # Horizontal line at y = 0\n    geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n    \n    # Coordinate limits and custom y-axis labels\n    coord_cartesian(ylim = c(ymin, ymax)) +\n    \n    # Annotate p-value text\n    annotate(\n      \\text\\, x = 1, y = ymin + 1,\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) +\n    \n    # Theme customization\n    theme_classic() +\n    theme(\n      axis.title = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    )+   \n    guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      ))\n  \n  \n  \n  \n  \n  fileName <- paste0(\\geneDensity_\\, note)\n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_geneDensity(gene.TSS.tb.plot, \\group_binaryGroup\\, ymax = 8)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
## geneDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$geneDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$geneDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_geneDensity <- function(temp.tb, note, ymin = 0, ymax = 10){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = \.\, fill = group, y = geneDensity)) +
# Set axis labels (no x-axis title)
labs(x = NULL, y = \Gene density within TAD\) +
scale_fill_manual(values = (rev(c(\#777777\,\#F28E2C\)))) +
# Violin plot with black outline, customized line width and end
introdataviz::geom_split_violin(linewidth = lineMedium * mmToLineUnit, lineend = \square\,
alpha = .4
) +
# Box plot with customized line width, square end, and outlier size
geom_boxplot(
width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE
) +
# Mean point in each group
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
# Horizontal line at y = 0
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
# Coordinate limits and custom y-axis labels
coord_cartesian(ylim = c(ymin, ymax)) +
# Annotate p-value text
annotate(
\text\, x = 1, y = ymin + 1,
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
) +
# Theme customization
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))
fileName <- paste0(\geneDensity_\, note)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_geneDensity(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 8)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n## geneDensity\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$geneDensity\n  distance2 <- (data %>% dplyr::filter(group == group2) )$geneDensity\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nplot_geneDensity <- function(temp.tb, note, ymin = 0, ymax = 10){\n  p12 <- round(getPvalWilcox(temp.tb, \\group1\\, \\group2\\), 5)\n  \n  p <- ggplot(temp.tb, aes(x = \\.\\, fill = group, y = geneDensity)) +\n    # Set axis labels (no x-axis title)\n    labs(x = NULL, y = \\Gene density within TAD\\) +\n    scale_fill_manual(values = (rev(c(\\#777777\\,\\#F28E2C\\)))) +\n    # Violin plot with black outline, customized line width and end\n    introdataviz::geom_split_violin(linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                                    alpha = .4\n    ) +\n    \n    # Box plot with customized line width, square end, and outlier size\n    geom_boxplot(\n      width = 0.3, color = \\black\\,\n      linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n      outlier.shape = NA,  alpha = 0.6, show.legend = FALSE\n    ) +\n    \n    # Mean point in each group\n    stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n    \n    # Horizontal line at y = 0\n    geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n    \n    # Coordinate limits and custom y-axis labels\n    coord_cartesian(ylim = c(ymin, ymax)) +\n    \n    # Annotate p-value text\n    annotate(\n      \\text\\, x = 1, y = ymin + 1,\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) +\n    \n    # Theme customization\n    theme_classic() +\n    theme(\n      axis.title = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    )+   \n    guides(\n      fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n      ))\n  \n  \n  \n  \n  \n  fileName <- paste0(\\geneDensity_\\, note)\n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\nplot_geneDensity(gene.TSS.tb.plot, \\group_binaryGroup\\, ymax = 8)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
## geneDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$geneDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$geneDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_geneDensity <- function(temp.tb, note, ymin = 0, ymax = 10){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = \.\, fill = group, y = geneDensity)) +
# Set axis labels (no x-axis title)
labs(x = NULL, y = \Gene density within TAD\) +
scale_fill_manual(values = (rev(c(\#777777\,\#F28E2C\)))) +
# Violin plot with black outline, customized line width and end
introdataviz::geom_split_violin(linewidth = lineMedium * mmToLineUnit, lineend = \square\,
alpha = .4
) +
# Box plot with customized line width, square end, and outlier size
geom_boxplot(
width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE
) +
# Mean point in each group
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
# Horizontal line at y = 0
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
# Coordinate limits and custom y-axis labels
coord_cartesian(ylim = c(ymin, ymax)) +
# Annotate p-value text
annotate(
\text\, x = 1, y = ymin + 1,
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
) +
# Theme customization
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))
fileName <- paste0(\geneDensity_\, note)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_geneDensity(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 8)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
###### Group
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<!-- rnb-output-begin 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 -->
## geneDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$geneDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$geneDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_geneDensity <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = geneDensity)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\geneDensity_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_geneDensity(gene.TSS.tb.plot, \group\, ymax = 10)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
## geneDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$geneDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$geneDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_geneDensity <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = geneDensity)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\geneDensity_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_geneDensity(gene.TSS.tb.plot, \group\, ymax = 10)
```
```
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##### geneDensity
###### binaryGroup
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## enhDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$enhDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$enhDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_enhDensity <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = group, y = enhDensity)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\enhDensity_\, note)
height <- 3
width <- 1.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_enhDensity(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 10)
<!-- rnb-output-end -->
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```r
```r
## enhDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$enhDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$enhDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_enhDensity <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p <- ggplot(temp.tb, aes(x = group, y = enhDensity)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\enhDensity_\, note)
height <- 3
width <- 1.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_enhDensity(gene.TSS.tb.plot, \group_binaryGroup\, ymax = 10)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###### Group
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
## enhDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$enhDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$enhDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_enhDensity <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = enhDensity)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\enhDensity_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_enhDensity(gene.TSS.tb.plot, \group\, ymax = 10)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
## enhDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$enhDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$enhDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_enhDensity <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = enhDensity)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\enhDensity_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_enhDensity(gene.TSS.tb.plot, \group\, ymax = 10)
```
```
<!-- rnb-source-end -->
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<!-- rnb-text-begin -->
##### enhDensity
###### Binary group
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## regDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$regDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$regDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_regDensity <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = regDensity)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\regDensity_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_regDensity(gene.TSS.tb.plot, \group\, ymax = 10)
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-->
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```r
```r
## regDensity
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$regDensity
distance2 <- (data %>% dplyr::filter(group == group2) )$regDensity
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
plot_regDensity <- function(temp.tb, note, ymin = 0, ymax = 2000000){
p12 <- round(getPvalWilcox(temp.tb, \group1\, \group2\), 5)
p15 <- round(getPvalWilcox(temp.tb, \group1\, \group5\), 5)
p18 <- round(getPvalWilcox(temp.tb, \group1\, \group8\), 5)
p25 <- round(getPvalWilcox(temp.tb, \group2\, \group5\), 5)
p28 <- round(getPvalWilcox(temp.tb, \group2\, \group8\), 5)
p58 <- round(getPvalWilcox(temp.tb, \group5\, \group8\), 5)
p <- ggplot(temp.tb, aes(x = group, y = regDensity)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = (ymin + ymax)/2 + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\regDensity_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
plot_regDensity(gene.TSS.tb.plot, \group\, ymax = 10)
```
```
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###### Group
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 -->\n\n```r\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj)\n\ntemp <- left_join(gene.TSS.tb, diff.RNA, by = c(\\ensembl\\ = \\ensembl_gene_id\\)) %>% dplyr::filter(!is.na(log2FoldChange),\n                                                                                                  !is.na(TAD))\n\n\ndata <- temp %>% group_by(TAD) %>%\n  dplyr::summarise(absAvgLog2FC = abs(mean(log2FoldChange)),\n                   avgLog2FC = mean(log2FoldChange),\n                   geneDensity = mean(geneDensity),\n                   enhDensity = mean(enhDensity),\n                   regDensity = mean(regDensity))\n\ndata$geneDensityGroup <- cut(\n  data$geneDensity,\n  breaks = quantile(data$geneDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),\n  include.lowest = TRUE,\n  labels = paste0(seq(0, 80, 20), \\-\\, seq(20, 100, 20), \\%\\)\n)\n\ndata$enhDensityGroup <- cut(\n  data$enhDensity,\n  breaks = quantile(data$enhDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),\n  include.lowest = TRUE,\n  labels = paste0(seq(0, 80, 20), \\-\\, seq(20, 100, 20), \\%\\)\n)\n\n\n### Ploting  grouping\n\np <- ggplot(data, aes(x = enhDensityGroup, y = enhDensity, fill = enhDensityGroup)) + \n  geom_boxplot(color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  coord_cartesian(ylim = c(0, quantile(data$enhDensity, 0.99))) +\n  labs(x = \\Enhancer Density Group\\ , y = \\TAD enhancer density\\) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    legend.position = \\none\\,\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- paste0(\\tadGroup_enhDensity\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\np <- ggplot(data, aes(x = geneDensityGroup, y = geneDensity, fill = geneDensityGroup)) + \n  geom_boxplot(color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  coord_cartesian(ylim = c(0, quantile(data$geneDensity, 0.99))) +\n  labs(x = \\Gene Density Group\\ , y = \\Gene density\\) +\n  scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  theme(\n    legend.position = \\none\\,\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\nfileName <- paste0(\\tadGroup_geneDensity\\)\nwidth <- 31*mmToInch\nheight <- 38*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### Plotting distribution - enh\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(enhDensityGroup ==group1) )$avgLog2FC\n  distance2 <- (data %>% dplyr::filter(enhDensityGroup ==group2) )$avgLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\n#### ALL\n# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))\n# p <- ggplot(data, aes(x = enhDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = enhDensityGroup))+ \n#   geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +\n#   coord_cartesian(ylim = c(-ylim, ylim)) +\n#   geom_hline(yintercept = 0) +   theme(legend.position = \\none\\) +\n#   scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n# \n# fileName <- paste0(\\tadGroup_enhDensity_avgLog2FC\\)\n# height <- 3\n# width <- 3\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n#### UP\ndata.plus <- data %>% dplyr::filter(avgLog2FC > 0)\nps01 <- round(getPvalWilcox(data.plus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.plus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.plus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.plus, \\60-80%\\, \\80-100%\\), 5)\n\n\n\np <- ggplot(data.plus, aes(x = enhDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = enhDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Enhancer density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = enhDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(0, 1.5)) +\n  annotate(\\text\\, x = 1, y = 1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\n\nfileName <- paste0(\\tadGroup_enhDensity_avgLog2FC_up\\)\nwidth <- panelSize(1.15)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#### DOWN\ndata.minus <- data %>% dplyr::filter(avgLog2FC < 0)\nps01 <- round(getPvalWilcox(data.minus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.minus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.minus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.minus, \\60-80%\\, \\80-100%\\), 5)\n\n\np <- ggplot(data.minus, aes(x = enhDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = enhDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Enhancer density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = enhDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(-1.5, 0))+\n  annotate(\\text\\, x = 1, y = -1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\nfileName <- paste0(\\tadGroup_enhDensity_avgLog2FC_down\\)\nwidth <- panelSize(1.175)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Plotting distribution - gene\n#### ALL\n# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))\n# p <- ggplot(data, aes(x = geneDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = geneDensityGroup))+ \n#   geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +\n#   coord_cartesian(ylim = c(-ylim, ylim)) +\n#   geom_hline(yintercept = 0) +   theme(legend.position = \\none\\) +\n#   scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n# \n# fileName <- paste0(\\tadGroup_geneDensity_avgLog2FC\\)\n# height <- 3\n# width <- 3\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(geneDensityGroup ==group1) )$avgLog2FC\n  distance2 <- (data %>% dplyr::filter(geneDensityGroup ==group2) )$avgLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\ndata.plus <- data %>% dplyr::filter(avgLog2FC > 0)\nps01 <- round(getPvalWilcox(data.plus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.plus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.plus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.plus, \\60-80%\\, \\80-100%\\), 5)\n\n\n\np <- ggplot(data.plus, aes(x = geneDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = geneDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Gene density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = geneDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(0, 1.5)) +\n  annotate(\\text\\, x = 1, y = 1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_up\\)\nwidth <- panelSize(1.15)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#### DOWN\ndata.minus <- data %>% dplyr::filter(avgLog2FC < 0)\nps01 <- round(getPvalWilcox(data.minus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.minus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.minus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.minus, \\60-80%\\, \\80-100%\\), 5)\n\n\np <- ggplot(data.minus, aes(x = geneDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = geneDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Gene density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = geneDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(-1.5, 0))+\n  annotate(\\text\\, x = 1, y = -1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_down\\)\nwidth <- panelSize(1.175)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### CDF plot\n\nks_result <- ks.test(\n  data %>% dplyr::filter(geneDensityGroup == \\0-20%\\) %>% pull(absAvgLog2FC),\n  data %>% dplyr::filter(geneDensityGroup == \\20-40%\\) %>% pull(absAvgLog2FC)\n)\n\n\np <- ggplot(data, aes(x = absAvgLog2FC, color = geneDensityGroup)) +\n  scale_color_manual(values = rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\n\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_cdf\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\ndata <- data.minus\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\0-20%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\20-40%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\20-40%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\40-60%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\40-60%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\60-80%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\60-80%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\80-100%\\]\nks.test(group1_data, group2_data)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj)
temp <- left_join(gene.TSS.tb, diff.RNA, by = c(\ensembl\ = \ensembl_gene_id\)) %>% dplyr::filter(!is.na(log2FoldChange),
!is.na(TAD))
data <- temp %>% group_by(TAD) %>%
dplyr::summarise(absAvgLog2FC = abs(mean(log2FoldChange)),
avgLog2FC = mean(log2FoldChange),
geneDensity = mean(geneDensity),
enhDensity = mean(enhDensity),
regDensity = mean(regDensity))
data$geneDensityGroup <- cut(
data$geneDensity,
breaks = quantile(data$geneDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),
include.lowest = TRUE,
labels = paste0(seq(0, 80, 20), \-\, seq(20, 100, 20), \%\)
)
data$enhDensityGroup <- cut(
data$enhDensity,
breaks = quantile(data$enhDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),
include.lowest = TRUE,
labels = paste0(seq(0, 80, 20), \-\, seq(20, 100, 20), \%\)
)
### Ploting grouping
p <- ggplot(data, aes(x = enhDensityGroup, y = enhDensity, fill = enhDensityGroup)) +
geom_boxplot(color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
coord_cartesian(ylim = c(0, quantile(data$enhDensity, 0.99))) +
labs(x = \Enhancer Density Group\ , y = \TAD enhancer density\) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
legend.position = \none\,
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\tadGroup_enhDensity\)
width <- panelSize(1.2)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
p <- ggplot(data, aes(x = geneDensityGroup, y = geneDensity, fill = geneDensityGroup)) +
geom_boxplot(color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
coord_cartesian(ylim = c(0, quantile(data$geneDensity, 0.99))) +
labs(x = \Gene Density Group\ , y = \Gene density\) +
scale_fill_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)) +
theme(
legend.position = \none\,
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\tadGroup_geneDensity\)
width <- 31*mmToInch
height <- 38*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Plotting distribution - enh
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(enhDensityGroup ==group1) )$avgLog2FC
distance2 <- (data %>% dplyr::filter(enhDensityGroup ==group2) )$avgLog2FC
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
#### ALL
# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))
# p <- ggplot(data, aes(x = enhDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = enhDensityGroup))+
# geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +
# coord_cartesian(ylim = c(-ylim, ylim)) +
# geom_hline(yintercept = 0) + theme(legend.position = \none\) +
# scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
#
# fileName <- paste0(\tadGroup_enhDensity_avgLog2FC\)
# height <- 3
# width <- 3
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
#### UP
data.plus <- data %>% dplyr::filter(avgLog2FC > 0)
ps01 <- round(getPvalWilcox(data.plus, \0-20%\, \20-40%\), 5)
ps12 <- round(getPvalWilcox(data.plus, \20-40%\, \40-60%\), 5)
ps23 <- round(getPvalWilcox(data.plus, \40-60%\, \60-80%\), 5)
ps34 <- round(getPvalWilcox(data.plus, \60-80%\, \80-100%\), 5)
p <- ggplot(data.plus, aes(x = enhDensityGroup, y = avgLog2FC)) +
geom_violin(aes(fill = enhDensityGroup),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE)+
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
labs(x = \Enhancer density group\ , y = \avg log2(fold change)\) +
stat_summary(
aes(group = enhDensityGroup), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ coord_cartesian(ylim = c(0, 1.5)) +
annotate(\text\, x = 1, y = 1, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\),
color = \black\, hjust = 0, size = 2)
fileName <- paste0(\tadGroup_enhDensity_avgLog2FC_up\)
width <- panelSize(1.15)*mmToInch
height <- panelSize(1.07)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#### DOWN
data.minus <- data %>% dplyr::filter(avgLog2FC < 0)
ps01 <- round(getPvalWilcox(data.minus, \0-20%\, \20-40%\), 5)
ps12 <- round(getPvalWilcox(data.minus, \20-40%\, \40-60%\), 5)
ps23 <- round(getPvalWilcox(data.minus, \40-60%\, \60-80%\), 5)
ps34 <- round(getPvalWilcox(data.minus, \60-80%\, \80-100%\), 5)
p <- ggplot(data.minus, aes(x = enhDensityGroup, y = avgLog2FC)) +
geom_violin(aes(fill = enhDensityGroup),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE)+
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
labs(x = \Enhancer density group\ , y = \avg log2(fold change)\) +
stat_summary(
aes(group = enhDensityGroup), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ coord_cartesian(ylim = c(-1.5, 0))+
annotate(\text\, x = 1, y = -1, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\),
color = \black\, hjust = 0, size = 2)
fileName <- paste0(\tadGroup_enhDensity_avgLog2FC_down\)
width <- panelSize(1.175)*mmToInch
height <- panelSize(1.07)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Plotting distribution - gene
#### ALL
# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))
# p <- ggplot(data, aes(x = geneDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = geneDensityGroup))+
# geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +
# coord_cartesian(ylim = c(-ylim, ylim)) +
# geom_hline(yintercept = 0) + theme(legend.position = \none\) +
# scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
#
# fileName <- paste0(\tadGroup_geneDensity_avgLog2FC\)
# height <- 3
# width <- 3
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(geneDensityGroup ==group1) )$avgLog2FC
distance2 <- (data %>% dplyr::filter(geneDensityGroup ==group2) )$avgLog2FC
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
data.plus <- data %>% dplyr::filter(avgLog2FC > 0)
ps01 <- round(getPvalWilcox(data.plus, \0-20%\, \20-40%\), 5)
ps12 <- round(getPvalWilcox(data.plus, \20-40%\, \40-60%\), 5)
ps23 <- round(getPvalWilcox(data.plus, \40-60%\, \60-80%\), 5)
ps34 <- round(getPvalWilcox(data.plus, \60-80%\, \80-100%\), 5)
p <- ggplot(data.plus, aes(x = geneDensityGroup, y = avgLog2FC)) +
geom_violin(aes(fill = geneDensityGroup),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE)+
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
labs(x = \Gene density group\ , y = \avg log2(fold change)\) +
stat_summary(
aes(group = geneDensityGroup), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ coord_cartesian(ylim = c(0, 1.5)) +
annotate(\text\, x = 1, y = 1, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\),
color = \black\, hjust = 0, size = 2)
fileName <- paste0(\tadGroup_geneDensity_avgLog2FC_up\)
width <- panelSize(1.15)*mmToInch
height <- panelSize(1.07)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#### DOWN
data.minus <- data %>% dplyr::filter(avgLog2FC < 0)
ps01 <- round(getPvalWilcox(data.minus, \0-20%\, \20-40%\), 5)
ps12 <- round(getPvalWilcox(data.minus, \20-40%\, \40-60%\), 5)
ps23 <- round(getPvalWilcox(data.minus, \40-60%\, \60-80%\), 5)
ps34 <- round(getPvalWilcox(data.minus, \60-80%\, \80-100%\), 5)
p <- ggplot(data.minus, aes(x = geneDensityGroup, y = avgLog2FC)) +
geom_violin(aes(fill = geneDensityGroup),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE)+
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
labs(x = \Gene density group\ , y = \avg log2(fold change)\) +
stat_summary(
aes(group = geneDensityGroup), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ coord_cartesian(ylim = c(-1.5, 0))+
annotate(\text\, x = 1, y = -1, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\),
color = \black\, hjust = 0, size = 2)
fileName <- paste0(\tadGroup_geneDensity_avgLog2FC_down\)
width <- panelSize(1.175)*mmToInch
height <- panelSize(1.07)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### CDF plot
ks_result <- ks.test(
data %>% dplyr::filter(geneDensityGroup == \0-20%\) %>% pull(absAvgLog2FC),
data %>% dplyr::filter(geneDensityGroup == \20-40%\) %>% pull(absAvgLog2FC)
)
p <- ggplot(data, aes(x = absAvgLog2FC, color = geneDensityGroup)) +
scale_color_manual(values = rev(c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS),
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\tadGroup_geneDensity_avgLog2FC_cdf\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
data <- data.minus
group1_data <- data$absAvgLog2FC[data$geneDensityGroup == \0-20%\]
group2_data <- data$absAvgLog2FC[data$geneDensityGroup == \20-40%\]
ks.test(group1_data, group2_data)
group1_data <- data$absAvgLog2FC[data$geneDensityGroup == \20-40%\]
group2_data <- data$absAvgLog2FC[data$geneDensityGroup == \40-60%\]
ks.test(group1_data, group2_data)
group1_data <- data$absAvgLog2FC[data$geneDensityGroup == \40-60%\]
group2_data <- data$absAvgLog2FC[data$geneDensityGroup == \60-80%\]
ks.test(group1_data, group2_data)
group1_data <- data$absAvgLog2FC[data$geneDensityGroup == \60-80%\]
group2_data <- data$absAvgLog2FC[data$geneDensityGroup == \80-100%\]
ks.test(group1_data, group2_data)
<!-- rnb-output-end -->
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dCwgbGluZWVuZCA9IFxcc3F1YXJlXFxcbiAgICApLFxuICAgIGF4aXMudGlja3MgPSBlbGVtZW50X2xpbmUoXG4gICAgICBjb2xvciA9IFxcIzAwMDAwMFxcLCBzaXplID0gbGluZVRoaWNrICogbW1Ub0xpbmVVbml0LCBsaW5lZW5kID0gXFxzcXVhcmVcXFxuICAgICksXG4gICAgcGFuZWwuYmFja2dyb3VuZCA9IGVsZW1lbnRfcmVjdChmaWxsID0gXFx0cmFuc3BhcmVudFxcKSxcbiAgICBsZWdlbmQudGV4dCA9IGVsZW1lbnRfdGV4dChmYW1pbHkgPSBmb250VHlwZSwgc2l6ZSA9IGZvbnRTaXplUyksXG4gICAgbGVnZW5kLnRpdGxlID0gZWxlbWVudF90ZXh0KGZhbWlseSA9IGZvbnRUeXBlLCBzaXplID0gZm9udFNpemVTKVxuICApXG5maWxlTmFtZSA8LSBwYXN0ZTAoXFx0YWRHcm91cF9nZW5lRGVuc2l0eVxcKVxud2lkdGggPC0gMzEqbW1Ub0luY2hcbmhlaWdodCA8LSAzOCptbVRvSW5jaFxucG5nKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnBuZ1xcKSksIHJlcyA9IDYwMCwgdW5pdCA9IFxcaW5cXCwgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuc3ZnbGl0ZShoZXJlKGZpZ0RpciwgcGFzdGUwKGZpbGVOYW1lLCBcXC5zdmdcXCkpLCAgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuXG5cbiMjIyBQbG90dGluZyBkaXN0cmlidXRpb24gLSBlbmhcbiBnZXRQdmFsV2lsY294IDwtIGZ1bmN0aW9uKGRhdGEsIGdyb3VwMSwgZ3JvdXAyKXtcbiAgZGlzdGFuY2UxIDwtIChkYXRhICU+JSBkcGx5cjo6ZmlsdGVyKGVuaERlbnNpdHlHcm91cCA9PWdyb3VwMSkgKSRhdmdMb2cyRkNcbiAgZGlzdGFuY2UyIDwtIChkYXRhICU+JSBkcGx5cjo6ZmlsdGVyKGVuaERlbnNpdHlHcm91cCA9PWdyb3VwMikgKSRhdmdMb2cyRkNcbiAgd2lsIDwtIHdpbGNveC50ZXN0KGRpc3RhbmNlMSwgZGlzdGFuY2UyKVxuICByZXR1cm4od2lsJHAudmFsdWUpXG59XG5cblxuIyMjIyBBTExcbiMgeWxpbSA8LSBtYXgoYWJzKHF1YW50aWxlKGRhdGEkYXZnTG9nMkZDLCAwLjAxKSksIGFicyhxdWFudGlsZShkYXRhJGF2Z0xvZzJGQywgMC45OSkpKVxuIyBwIDwtIGdncGxvdChkYXRhLCBhZXMoeCA9IGVuaERlbnNpdHlHcm91cCwgeSA9IGF2Z0xvZzJGQykpICsgZ2VvbV92aW9saW4oYWVzKGZpbGwgPSBlbmhEZW5zaXR5R3JvdXApKSsgXG4jICAgZ2VvbV9ib3hwbG90KG91dGxpZXIuc2hhcGUgPSBOQSwgd2lkdGggPSAwLjEpICsgdGhlbWVfY2xhc3NpYygpICtcbiMgICBjb29yZF9jYXJ0ZXNpYW4oeWxpbSA9IGMoLXlsaW0sIHlsaW0pKSArXG4jICAgZ2VvbV9obGluZSh5aW50ZXJjZXB0ID0gMCkgKyAgIHRoZW1lKGxlZ2VuZC5wb3NpdGlvbiA9IFxcbm9uZVxcKSArXG4jICAgc2NhbGVfZmlsbF9tYW51YWwodmFsdWVzID0gYyhcXCNkOWQ5ZDlcXCwgXFwjYmRiZGJkXFwsIFxcIzk2OTY5NlxcLCBcXCM3MzczNzNcXCwgXFwjNTI1MjUyXFwpKVxuIyBcbiMgZmlsZU5hbWUgPC0gcGFzdGUwKFxcdGFkR3JvdXBfZW5oRGVuc2l0eV9hdmdMb2cyRkNcXClcbiMgaGVpZ2h0IDwtIDNcbiMgd2lkdGggPC0gM1xuIyBwbmcoaGVyZShmaWdEaXIsIHBhc3RlMChmaWxlTmFtZSwgXFwucG5nXFwpKSwgcmVzID0gNjAwLCB1bml0ID0gXFxpblxcLCBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG4jIHByaW50KHApXG4jIGRldi5vZmYoKVxuIyBzdmdsaXRlKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnN2Z1xcKSksICBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG4jIHByaW50KHApXG4jIGRldi5vZmYoKVxuIyMjIyBVUFxuZGF0YS5wbHVzIDwtIGRhdGEgJT4lIGRwbHlyOjpmaWx0ZXIoYXZnTG9nMkZDID4gMClcbnBzMDEgPC0gcm91bmQoZ2V0UHZhbFdpbGNveChkYXRhLnBsdXMsIFxcMC0yMCVcXCwgXFwyMC00MCVcXCksIDUpXG5wczEyIDwtIHJvdW5kKGdldFB2YWxXaWxjb3goZGF0YS5wbHVzLCBcXDIwLTQwJVxcLCBcXDQwLTYwJVxcKSwgNSlcbnBzMjMgPC0gcm91bmQoZ2V0UHZhbFdpbGNveChkYXRhLnBsdXMsIFxcNDAtNjAlXFwsIFxcNjAtODAlXFwpLCA1KVxucHMzNCA8LSByb3VuZChnZXRQdmFsV2lsY294KGRhdGEucGx1cywgXFw2MC04MCVcXCwgXFw4MC0xMDAlXFwpLCA1KVxuXG5cblxucCA8LSBnZ3Bsb3QoZGF0YS5wbHVzLCBhZXMoeCA9IGVuaERlbnNpdHlHcm91cCwgeSA9IGF2Z0xvZzJGQykpICsgXG4gIGdlb21fdmlvbGluKGFlcyhmaWxsID0gZW5oRGVuc2l0eUdyb3VwKSxcbiAgICAgICAgICAgICAgY29sb3IgPSBcXGJsYWNrXFwsXG4gICAgICAgICAgICAgIGxpbmV3aWR0aCA9IGxpbmVUaGljayAqIG1tVG9MaW5lVW5pdCwgbGluZWVuZCA9IFxcc3F1YXJlXFwsXG4gICAgICAgICAgICAgIHNob3cubGVnZW5kID0gRkFMU0UpKyBcbiAgZ2VvbV9ib3hwbG90KHdpZHRoID0gMC4xLCBjb2xvciA9IFxcYmxhY2tcXCxcbiAgICAgICAgICAgICAgIGxpbmV3aWR0aCA9IGxpbmVUaGljayAqIG1tVG9MaW5lVW5pdCwgbGluZWVuZCA9IFxcc3F1YXJlXFwsXG4gICAgICAgICAgICAgICBvdXRsaWVyLnNoYXBlID0gTkEpICsgdGhlbWVfY2xhc3NpYygpICtcbiAgZ2VvbV9obGluZSh5aW50ZXJjZXB0ID0gMCwgbGluZXdpZHRoID0gbGluZVRoaWNrICogbW1Ub0xpbmVVbml0KSArXG4gIGxhYnMoeCA9IFxcRW5oYW5jZXIgZGVuc2l0eSBncm91cFxcICwgeSA9IFxcYXZnIGxvZzIoZm9sZCBjaGFuZ2UpXFwpICtcbiAgc3RhdF9zdW1tYXJ5KFxuICAgIGFlcyhncm91cCA9IGVuaERlbnNpdHlHcm91cCksIGZ1biA9IG1lYW4sXG4gICAgZ2VvbSA9IFxccG9pbnRcXCwgc2hhcGUgPSAyMSwgc2l6ZSA9IDEsXG4gICAgZmlsbCA9IFxccmVkXFwsIGNvbG9yID0gXFxibGFja1xcXG4gICkgK1xuICBzY2FsZV9maWxsX21hbnVhbCh2YWx1ZXMgPSBjKFxcI0Q5RDlEOVxcLCBcXCNCRkJGQkZcXCwgXFwjQTZBNkE2XFwsIFxcIzhDOEM4Q1xcLCBcXCM3MzczNzNcXCkpICtcbiAgdGhlbWUoXG4gICAgYXhpcy50aXRsZSA9IGVsZW1lbnRfdGV4dChcbiAgICAgIHNpemUgPSBmb250U2l6ZVMsIGZhbWlseSA9IGZvbnRUeXBlLCBjb2xvciA9IFxcIzAwMDAwMFxcXG4gICAgKSxcbiAgICBheGlzLnRleHQgPSBlbGVtZW50X3RleHQoXG4gICAgICBzaXplID0gZm9udFNpemVTLCBmYW1pbHkgPSBmb250VHlwZSwgY29sb3IgPSBcXCMwMDAwMDBcXFxuICAgICksXG4gICAgYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoXG4gICAgICBhbmdsZSA9IDQ1LCBoanVzdCA9IDEsIHZqdXN0ID0gMVxuICAgICksXG4gICAgYXhpcy5saW5lID0gZWxlbWVudF9saW5lKFxuICAgICAgY29sb3IgPSBcXCMwMDAwMDBcXCwgc2l6ZSA9IGxpbmVUaGljayAqIG1tVG9MaW5lVW5pdCwgbGluZWVuZCA9IFxcc3F1YXJlXFxcbiAgICApLFxuICAgIGF4aXMudGlja3MgPSBlbGVtZW50X2xpbmUoXG4gICAgICBjb2xvciA9IFxcIzAwMDAwMFxcLCBzaXplID0gbGluZVRoaWNrICogbW1Ub0xpbmVVbml0LCBsaW5lZW5kID0gXFxzcXVhcmVcXFxuICAgICksXG4gICAgcGFuZWwuYmFja2dyb3VuZCA9IGVsZW1lbnRfcmVjdChmaWxsID0gXFx0cmFuc3BhcmVudFxcKSxcbiAgICBsZWdlbmQudGV4dCA9IGVsZW1lbnRfdGV4dChmYW1pbHkgPSBmb250VHlwZSwgc2l6ZSA9IGZvbnRTaXplUyksXG4gICAgbGVnZW5kLnRpdGxlID0gZWxlbWVudF90ZXh0KGZhbWlseSA9IGZvbnRUeXBlLCBzaXplID0gZm9udFNpemVTKVxu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vgLog2FC)) + geom_violin(aes(fill = geneDensityGroup))+ \n#   geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +\n#   coord_cartesian(ylim = c(-ylim, ylim)) +\n#   geom_hline(yintercept = 0) +   theme(legend.position = \\none\\) +\n#   scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n# \n# fileName <- paste0(\\tadGroup_geneDensity_avgLog2FC\\)\n# height <- 3\n# width <- 3\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(geneDensityGroup ==group1) )$avgLog2FC\n  distance2 <- (data %>% dplyr::filter(geneDensityGroup ==group2) )$avgLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\ndata.plus <- data %>% dplyr::filter(avgLog2FC > 0)\nps01 <- round(getPvalWilcox(data.plus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.plus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.plus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.plus, \\60-80%\\, \\80-100%\\), 5)\n\n\n\np <- ggplot(data.plus, aes(x = geneDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = geneDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Gene density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = geneDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(0, 1.5)) +\n  annotate(\\text\\, x = 1, y = 1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_up\\)\nwidth <- panelSize(1.15)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#### DOWN\ndata.minus <- data %>% dplyr::filter(avgLog2FC < 0)\nps01 <- round(getPvalWilcox(data.minus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.minus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.minus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.minus, \\60-80%\\, \\80-100%\\), 5)\n\n\np <- ggplot(data.minus, aes(x = geneDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = geneDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Gene density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = geneDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(-1.5, 0))+\n  annotate(\\text\\, x = 1, y = -1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_down\\)\nwidth <- panelSize(1.175)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### CDF plot\n\nks_result <- ks.test(\n  data %>% dplyr::filter(geneDensityGroup == \\0-20%\\) %>% pull(absAvgLog2FC),\n  data %>% dplyr::filter(geneDensityGroup == \\20-40%\\) %>% pull(absAvgLog2FC)\n)\n\n\np <- ggplot(data, aes(x = absAvgLog2FC, color = geneDensityGroup)) +\n  scale_color_manual(values = rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\n\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_cdf\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\ndata <- data.minus\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\0-20%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\20-40%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\20-40%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\40-60%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\40-60%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\60-80%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\60-80%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\80-100%\\]\nks.test(group1_data, group2_data)\n\n\n```\n```"} -->\n\n```r\n```r\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj)\n\ntemp <- left_join(gene.TSS.tb, diff.RNA, by = c(\\ensembl\\ = \\ensembl_gene_id\\)) %>% dplyr::filter(!is.na(log2FoldChange),\n                                                                                                  !is.na(TAD))\n\n\ndata <- temp %>% group_by(TAD) %>%\n  dplyr::summarise(absAvgLog2FC = abs(mean(log2FoldChange)),\n                   avgLog2FC = mean(log2FoldChange),\n                   geneDensity = mean(geneDensity),\n                   enhDensity = mean(enhDensity),\n                   regDensity = mean(regDensity))\n\ndata$geneDensityGroup <- cut(\n  data$geneDensity,\n  breaks = quantile(data$geneDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),\n  include.lowest = TRUE,\n  labels = paste0(seq(0, 80, 20), \\-\\, seq(20, 100, 20), \\%\\)\n)\n\ndata$enhDensityGroup <- cut(\n  data$enhDensity,\n  breaks = quantile(data$enhDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),\n  include.lowest = TRUE,\n  labels = paste0(seq(0, 80, 20), \\-\\, seq(20, 100, 20), \\%\\)\n)\n\n\n### Ploting  grouping\n\np <- ggplot(data, aes(x = enhDensityGroup, y = enhDensity, fill = enhDensityGroup)) + \n  geom_boxplot(color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  coord_cartesian(ylim = c(0, quantile(data$enhDensity, 0.99))) +\n  labs(x = \\Enhancer Density Group\\ , y = \\TAD enhancer density\\) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    legend.position = \\none\\,\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- paste0(\\tadGroup_enhDensity\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\np <- ggplot(data, aes(x = geneDensityGroup, y = geneDensity, fill = geneDensityGroup)) + \n  geom_boxplot(color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  coord_cartesian(ylim = c(0, quantile(data$geneDensity, 0.99))) +\n  labs(x = \\Gene Density Group\\ , y = \\Gene density\\) +\n  scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  theme(\n    legend.position = \\none\\,\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\nfileName <- paste0(\\tadGroup_geneDensity\\)\nwidth <- 31*mmToInch\nheight <- 38*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### Plotting distribution - enh\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(enhDensityGroup ==group1) )$avgLog2FC\n  distance2 <- (data %>% dplyr::filter(enhDensityGroup ==group2) )$avgLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\n#### ALL\n# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))\n# p <- ggplot(data, aes(x = enhDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = enhDensityGroup))+ \n#   geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +\n#   coord_cartesian(ylim = c(-ylim, ylim)) +\n#   geom_hline(yintercept = 0) +   theme(legend.position = \\none\\) +\n#   scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n# \n# fileName <- paste0(\\tadGroup_enhDensity_avgLog2FC\\)\n# height <- 3\n# width <- 3\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n#### UP\ndata.plus <- data %>% dplyr::filter(avgLog2FC > 0)\nps01 <- round(getPvalWilcox(data.plus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.plus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.plus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.plus, \\60-80%\\, \\80-100%\\), 5)\n\n\n\np <- ggplot(data.plus, aes(x = enhDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = enhDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Enhancer density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = enhDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(0, 1.5)) +\n  annotate(\\text\\, x = 1, y = 1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\n\nfileName <- paste0(\\tadGroup_enhDensity_avgLog2FC_up\\)\nwidth <- panelSize(1.15)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#### DOWN\ndata.minus <- data %>% dplyr::filter(avgLog2FC < 0)\nps01 <- round(getPvalWilcox(data.minus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.minus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.minus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.minus, \\60-80%\\, \\80-100%\\), 5)\n\n\np <- ggplot(data.minus, aes(x = enhDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = enhDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Enhancer density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = enhDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(-1.5, 0))+\n  annotate(\\text\\, x = 1, y = -1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\nfileName <- paste0(\\tadGroup_enhDensity_avgLog2FC_down\\)\nwidth <- panelSize(1.175)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Plotting distribution - gene\n#### ALL\n# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))\n# p <- ggplot(data, aes(x = geneDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = geneDensityGroup))+ \n#   geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +\n#   coord_cartesian(ylim = c(-ylim, ylim)) +\n#   geom_hline(yintercept = 0) +   theme(legend.position = \\none\\) +\n#   scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n# \n# fileName <- paste0(\\tadGroup_geneDensity_avgLog2FC\\)\n# height <- 3\n# width <- 3\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(geneDensityGroup ==group1) )$avgLog2FC\n  distance2 <- (data %>% dplyr::filter(geneDensityGroup ==group2) )$avgLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\ndata.plus <- data %>% dplyr::filter(avgLog2FC > 0)\nps01 <- round(getPvalWilcox(data.plus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.plus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.plus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.plus, \\60-80%\\, \\80-100%\\), 5)\n\n\n\np <- ggplot(data.plus, aes(x = geneDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = geneDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Gene density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = geneDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(0, 1.5)) +\n  annotate(\\text\\, x = 1, y = 1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_up\\)\nwidth <- panelSize(1.15)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#### DOWN\ndata.minus <- data %>% dplyr::filter(avgLog2FC < 0)\nps01 <- round(getPvalWilcox(data.minus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.minus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.minus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.minus, \\60-80%\\, \\80-100%\\), 5)\n\n\np <- ggplot(data.minus, aes(x = geneDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = geneDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Gene density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = geneDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(-1.5, 0))+\n  annotate(\\text\\, x = 1, y = -1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_down\\)\nwidth <- panelSize(1.175)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### CDF plot\n\nks_result <- ks.test(\n  data %>% dplyr::filter(geneDensityGroup == \\0-20%\\) %>% pull(absAvgLog2FC),\n  data %>% dplyr::filter(geneDensityGroup == \\20-40%\\) %>% pull(absAvgLog2FC)\n)\n\n\np <- ggplot(data, aes(x = absAvgLog2FC, color = geneDensityGroup)) +\n  scale_color_manual(values = rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\n\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_cdf\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\ndata <- data.minus\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\0-20%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\20-40%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\20-40%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\40-60%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\40-60%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\60-80%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\60-80%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\80-100%\\]\nks.test(group1_data, group2_data)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj)\n\ntemp <- left_join(gene.TSS.tb, diff.RNA, by = c(\\ensembl\\ = \\ensembl_gene_id\\)) %>% dplyr::filter(!is.na(log2FoldChange),\n                                                                                                  !is.na(TAD))\n\n\ndata <- temp %>% group_by(TAD) %>%\n  dplyr::summarise(absAvgLog2FC = abs(mean(log2FoldChange)),\n                   avgLog2FC = mean(log2FoldChange),\n                   geneDensity = mean(geneDensity),\n                   enhDensity = mean(enhDensity),\n                   regDensity = mean(regDensity))\n\ndata$geneDensityGroup <- cut(\n  data$geneDensity,\n  breaks = quantile(data$geneDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),\n  include.lowest = TRUE,\n  labels = paste0(seq(0, 80, 20), \\-\\, seq(20, 100, 20), \\%\\)\n)\n\ndata$enhDensityGroup <- cut(\n  data$enhDensity,\n  breaks = quantile(data$enhDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),\n  include.lowest = TRUE,\n  labels = paste0(seq(0, 80, 20), \\-\\, seq(20, 100, 20), \\%\\)\n)\n\n\n### Ploting  grouping\n\np <- ggplot(data, aes(x = enhDensityGroup, y = enhDensity, fill = enhDensityGroup)) + \n  geom_boxplot(color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  coord_cartesian(ylim = c(0, quantile(data$enhDensity, 0.99))) +\n  labs(x = \\Enhancer Density Group\\ , y = \\TAD enhancer density\\) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    legend.position = \\none\\,\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- paste0(\\tadGroup_enhDensity\\)\nwidth <- panelSize(1.2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\np <- ggplot(data, aes(x = geneDensityGroup, y = geneDensity, fill = geneDensityGroup)) + \n  geom_boxplot(color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  coord_cartesian(ylim = c(0, quantile(data$geneDensity, 0.99))) +\n  labs(x = \\Gene Density Group\\ , y = \\Gene density\\) +\n  scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  theme(\n    legend.position = \\none\\,\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\nfileName <- paste0(\\tadGroup_geneDensity\\)\nwidth <- 31*mmToInch\nheight <- 38*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### Plotting distribution - enh\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(enhDensityGroup ==group1) )$avgLog2FC\n  distance2 <- (data %>% dplyr::filter(enhDensityGroup ==group2) )$avgLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\n#### ALL\n# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))\n# p <- ggplot(data, aes(x = enhDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = enhDensityGroup))+ \n#   geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +\n#   coord_cartesian(ylim = c(-ylim, ylim)) +\n#   geom_hline(yintercept = 0) +   theme(legend.position = \\none\\) +\n#   scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n# \n# fileName <- paste0(\\tadGroup_enhDensity_avgLog2FC\\)\n# height <- 3\n# width <- 3\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n#### UP\ndata.plus <- data %>% dplyr::filter(avgLog2FC > 0)\nps01 <- round(getPvalWilcox(data.plus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.plus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.plus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.plus, \\60-80%\\, \\80-100%\\), 5)\n\n\n\np <- ggplot(data.plus, aes(x = enhDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = enhDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Enhancer density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = enhDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(0, 1.5)) +\n  annotate(\\text\\, x = 1, y = 1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\n\nfileName <- paste0(\\tadGroup_enhDensity_avgLog2FC_up\\)\nwidth <- panelSize(1.15)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#### DOWN\ndata.minus <- data %>% dplyr::filter(avgLog2FC < 0)\nps01 <- round(getPvalWilcox(data.minus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.minus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.minus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.minus, \\60-80%\\, \\80-100%\\), 5)\n\n\np <- ggplot(data.minus, aes(x = enhDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = enhDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Enhancer density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = enhDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(-1.5, 0))+\n  annotate(\\text\\, x = 1, y = -1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\nfileName <- paste0(\\tadGroup_enhDensity_avgLog2FC_down\\)\nwidth <- panelSize(1.175)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Plotting distribution - gene\n#### ALL\n# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))\n# p <- ggplot(data, aes(x = geneDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = geneDensityGroup))+ \n#   geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +\n#   coord_cartesian(ylim = c(-ylim, ylim)) +\n#   geom_hline(yintercept = 0) +   theme(legend.position = \\none\\) +\n#   scale_fill_manual(values = c(\\#d9d9d9\\, \\#bdbdbd\\, \\#969696\\, \\#737373\\, \\#525252\\))\n# \n# fileName <- paste0(\\tadGroup_geneDensity_avgLog2FC\\)\n# height <- 3\n# width <- 3\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n getPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(geneDensityGroup ==group1) )$avgLog2FC\n  distance2 <- (data %>% dplyr::filter(geneDensityGroup ==group2) )$avgLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\ndata.plus <- data %>% dplyr::filter(avgLog2FC > 0)\nps01 <- round(getPvalWilcox(data.plus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.plus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.plus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.plus, \\60-80%\\, \\80-100%\\), 5)\n\n\n\np <- ggplot(data.plus, aes(x = geneDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = geneDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Gene density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = geneDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(0, 1.5)) +\n  annotate(\\text\\, x = 1, y = 1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_up\\)\nwidth <- panelSize(1.15)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n#### DOWN\ndata.minus <- data %>% dplyr::filter(avgLog2FC < 0)\nps01 <- round(getPvalWilcox(data.minus, \\0-20%\\, \\20-40%\\), 5)\nps12 <- round(getPvalWilcox(data.minus, \\20-40%\\, \\40-60%\\), 5)\nps23 <- round(getPvalWilcox(data.minus, \\40-60%\\, \\60-80%\\), 5)\nps34 <- round(getPvalWilcox(data.minus, \\60-80%\\, \\80-100%\\), 5)\n\n\np <- ggplot(data.minus, aes(x = geneDensityGroup, y = avgLog2FC)) + \n  geom_violin(aes(fill = geneDensityGroup),\n              color = \\black\\,\n              linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE)+ \n  geom_boxplot(width = 0.1, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  labs(x = \\Gene density group\\ , y = \\avg log2(fold change)\\) +\n  stat_summary(\n    aes(group = geneDensityGroup), fun = mean,\n    geom = \\point\\, shape = 21, size = 1,\n    fill = \\red\\, color = \\black\\\n  ) +\n  scale_fill_manual(values = c(\\#D9D9D9\\, \\#BFBFBF\\, \\#A6A6A6\\, \\#8C8C8C\\, \\#737373\\)) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+ coord_cartesian(ylim = c(-1.5, 0))+\n  annotate(\\text\\, x = 1, y = -1, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2)\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_down\\)\nwidth <- panelSize(1.175)*mmToInch\nheight <- panelSize(1.07)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### CDF plot\n\nks_result <- ks.test(\n  data %>% dplyr::filter(geneDensityGroup == \\0-20%\\) %>% pull(absAvgLog2FC),\n  data %>% dplyr::filter(geneDensityGroup == \\20-40%\\) %>% pull(absAvgLog2FC)\n)\n\n\np <- ggplot(data, aes(x = absAvgLog2FC, color = geneDensityGroup)) +\n  scale_color_manual(values = rev(c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))) +\n  stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\ ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    ) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))\n\n\nfileName <- paste0(\\tadGroup_geneDensity_avgLog2FC_cdf\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\ndata <- data.minus\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\0-20%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\20-40%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\20-40%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\40-60%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\40-60%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\60-80%\\]\nks.test(group1_data, group2_data)\n\ngroup1_data <- data$absAvgLog2FC[data$geneDensityGroup == \\60-80%\\]\ngroup2_data <- data$absAvgLog2FC[data$geneDensityGroup == \\80-100%\\]\nks.test(group1_data, group2_data)\n\n\n```\n```"} -->
```r
```r
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj)
temp <- left_join(gene.TSS.tb, diff.RNA, by = c(\ensembl\ = \ensembl_gene_id\)) %>% dplyr::filter(!is.na(log2FoldChange),
!is.na(TAD))
data <- temp %>% group_by(TAD) %>%
dplyr::summarise(absAvgLog2FC = abs(mean(log2FoldChange)),
avgLog2FC = mean(log2FoldChange),
geneDensity = mean(geneDensity),
enhDensity = mean(enhDensity),
regDensity = mean(regDensity))
data$geneDensityGroup <- cut(
data$geneDensity,
breaks = quantile(data$geneDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),
include.lowest = TRUE,
labels = paste0(seq(0, 80, 20), \-\, seq(20, 100, 20), \%\)
)
data$enhDensityGroup <- cut(
data$enhDensity,
breaks = quantile(data$enhDensity, probs = seq(0, 1, 0.2), na.rm = TRUE),
include.lowest = TRUE,
labels = paste0(seq(0, 80, 20), \-\, seq(20, 100, 20), \%\)
)
### Ploting grouping
p <- ggplot(data, aes(x = enhDensityGroup, y = enhDensity, fill = enhDensityGroup)) +
geom_boxplot(color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
coord_cartesian(ylim = c(0, quantile(data$enhDensity, 0.99))) +
labs(x = \Enhancer Density Group\ , y = \TAD enhancer density\) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
legend.position = \none\,
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\tadGroup_enhDensity\)
width <- panelSize(1.2)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
p <- ggplot(data, aes(x = geneDensityGroup, y = geneDensity, fill = geneDensityGroup)) +
geom_boxplot(color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
coord_cartesian(ylim = c(0, quantile(data$geneDensity, 0.99))) +
labs(x = \Gene Density Group\ , y = \Gene density\) +
scale_fill_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)) +
theme(
legend.position = \none\,
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\tadGroup_geneDensity\)
width <- 31*mmToInch
height <- 38*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Plotting distribution - enh
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(enhDensityGroup ==group1) )$avgLog2FC
distance2 <- (data %>% dplyr::filter(enhDensityGroup ==group2) )$avgLog2FC
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
#### ALL
# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))
# p <- ggplot(data, aes(x = enhDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = enhDensityGroup))+
# geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +
# coord_cartesian(ylim = c(-ylim, ylim)) +
# geom_hline(yintercept = 0) + theme(legend.position = \none\) +
# scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
#
# fileName <- paste0(\tadGroup_enhDensity_avgLog2FC\)
# height <- 3
# width <- 3
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
#### UP
data.plus <- data %>% dplyr::filter(avgLog2FC > 0)
ps01 <- round(getPvalWilcox(data.plus, \0-20%\, \20-40%\), 5)
ps12 <- round(getPvalWilcox(data.plus, \20-40%\, \40-60%\), 5)
ps23 <- round(getPvalWilcox(data.plus, \40-60%\, \60-80%\), 5)
ps34 <- round(getPvalWilcox(data.plus, \60-80%\, \80-100%\), 5)
p <- ggplot(data.plus, aes(x = enhDensityGroup, y = avgLog2FC)) +
geom_violin(aes(fill = enhDensityGroup),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE)+
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
labs(x = \Enhancer density group\ , y = \avg log2(fold change)\) +
stat_summary(
aes(group = enhDensityGroup), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ coord_cartesian(ylim = c(0, 1.5)) +
annotate(\text\, x = 1, y = 1, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\),
color = \black\, hjust = 0, size = 2)
fileName <- paste0(\tadGroup_enhDensity_avgLog2FC_up\)
width <- panelSize(1.15)*mmToInch
height <- panelSize(1.07)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#### DOWN
data.minus <- data %>% dplyr::filter(avgLog2FC < 0)
ps01 <- round(getPvalWilcox(data.minus, \0-20%\, \20-40%\), 5)
ps12 <- round(getPvalWilcox(data.minus, \20-40%\, \40-60%\), 5)
ps23 <- round(getPvalWilcox(data.minus, \40-60%\, \60-80%\), 5)
ps34 <- round(getPvalWilcox(data.minus, \60-80%\, \80-100%\), 5)
p <- ggplot(data.minus, aes(x = enhDensityGroup, y = avgLog2FC)) +
geom_violin(aes(fill = enhDensityGroup),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE)+
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
labs(x = \Enhancer density group\ , y = \avg log2(fold change)\) +
stat_summary(
aes(group = enhDensityGroup), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ coord_cartesian(ylim = c(-1.5, 0))+
annotate(\text\, x = 1, y = -1, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\),
color = \black\, hjust = 0, size = 2)
fileName <- paste0(\tadGroup_enhDensity_avgLog2FC_down\)
width <- panelSize(1.175)*mmToInch
height <- panelSize(1.07)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Plotting distribution - gene
#### ALL
# ylim <- max(abs(quantile(data$avgLog2FC, 0.01)), abs(quantile(data$avgLog2FC, 0.99)))
# p <- ggplot(data, aes(x = geneDensityGroup, y = avgLog2FC)) + geom_violin(aes(fill = geneDensityGroup))+
# geom_boxplot(outlier.shape = NA, width = 0.1) + theme_classic() +
# coord_cartesian(ylim = c(-ylim, ylim)) +
# geom_hline(yintercept = 0) + theme(legend.position = \none\) +
# scale_fill_manual(values = c(\#d9d9d9\, \#bdbdbd\, \#969696\, \#737373\, \#525252\))
#
# fileName <- paste0(\tadGroup_geneDensity_avgLog2FC\)
# height <- 3
# width <- 3
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(geneDensityGroup ==group1) )$avgLog2FC
distance2 <- (data %>% dplyr::filter(geneDensityGroup ==group2) )$avgLog2FC
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
data.plus <- data %>% dplyr::filter(avgLog2FC > 0)
ps01 <- round(getPvalWilcox(data.plus, \0-20%\, \20-40%\), 5)
ps12 <- round(getPvalWilcox(data.plus, \20-40%\, \40-60%\), 5)
ps23 <- round(getPvalWilcox(data.plus, \40-60%\, \60-80%\), 5)
ps34 <- round(getPvalWilcox(data.plus, \60-80%\, \80-100%\), 5)
p <- ggplot(data.plus, aes(x = geneDensityGroup, y = avgLog2FC)) +
geom_violin(aes(fill = geneDensityGroup),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE)+
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
labs(x = \Gene density group\ , y = \avg log2(fold change)\) +
stat_summary(
aes(group = geneDensityGroup), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ coord_cartesian(ylim = c(0, 1.5)) +
annotate(\text\, x = 1, y = 1, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\),
color = \black\, hjust = 0, size = 2)
fileName <- paste0(\tadGroup_geneDensity_avgLog2FC_up\)
width <- panelSize(1.15)*mmToInch
height <- panelSize(1.07)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#### DOWN
data.minus <- data %>% dplyr::filter(avgLog2FC < 0)
ps01 <- round(getPvalWilcox(data.minus, \0-20%\, \20-40%\), 5)
ps12 <- round(getPvalWilcox(data.minus, \20-40%\, \40-60%\), 5)
ps23 <- round(getPvalWilcox(data.minus, \40-60%\, \60-80%\), 5)
ps34 <- round(getPvalWilcox(data.minus, \60-80%\, \80-100%\), 5)
p <- ggplot(data.minus, aes(x = geneDensityGroup, y = avgLog2FC)) +
geom_violin(aes(fill = geneDensityGroup),
color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE)+
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
labs(x = \Gene density group\ , y = \avg log2(fold change)\) +
stat_summary(
aes(group = geneDensityGroup), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
scale_fill_manual(values = c(\#D9D9D9\, \#BFBFBF\, \#A6A6A6\, \#8C8C8C\, \#737373\)) +
theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+ coord_cartesian(ylim = c(-1.5, 0))+
annotate(\text\, x = 1, y = -1, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\),
color = \black\, hjust = 0, size = 2)
fileName <- paste0(\tadGroup_geneDensity_avgLog2FC_down\)
width <- panelSize(1.175)*mmToInch
height <- panelSize(1.07)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### CDF plot
ks_result <- ks.test(
data %>% dplyr::filter(geneDensityGroup == \0-20%\) %>% pull(absAvgLog2FC),
data %>% dplyr::filter(geneDensityGroup == \20-40%\) %>% pull(absAvgLog2FC)
)
p <- ggplot(data, aes(x = absAvgLog2FC, color = geneDensityGroup)) +
scale_color_manual(values = rev(c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS),
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\tadGroup_geneDensity_avgLog2FC_cdf\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
data <- data.minus
group1_data <- data$absAvgLog2FC[data$geneDensityGroup == \0-20%\]
group2_data <- data$absAvgLog2FC[data$geneDensityGroup == \20-40%\]
ks.test(group1_data, group2_data)
group1_data <- data$absAvgLog2FC[data$geneDensityGroup == \20-40%\]
group2_data <- data$absAvgLog2FC[data$geneDensityGroup == \40-60%\]
ks.test(group1_data, group2_data)
group1_data <- data$absAvgLog2FC[data$geneDensityGroup == \40-60%\]
group2_data <- data$absAvgLog2FC[data$geneDensityGroup == \60-80%\]
ks.test(group1_data, group2_data)
group1_data <- data$absAvgLog2FC[data$geneDensityGroup == \60-80%\]
group2_data <- data$absAvgLog2FC[data$geneDensityGroup == \80-100%\]
ks.test(group1_data, group2_data)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### regdensity
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\nconsensus.loop.anno.tb <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n\n# Import insulation score calculated with python\nresultDir <- here(\\../../result\\)\ninsScore.DMSO <- fread(here(resultDir, \\TAD\\, \\insulationScore_25kb_G1DMSO.tsv\\)) %>% \n  dplyr::select(c(\\chrom\\, \\start\\, \\end\\, \\log2_insulation_score_125000\\)) %>%\n  dplyr::mutate(binID = paste(chrom, start, end, sep = \\_\\))\ncolnames(insScore.DMSO) <- c(\\chrom\\, \\start\\, \\end\\, \\insulationScore\\, \\binID\\)\ninsScore.dTAG <- fread(here(resultDir, \\TAD\\, \\insulationScore_25kb_G1dTAG.tsv\\)) %>% \n  dplyr::select(c(\\chrom\\, \\start\\, \\end\\, \\log2_insulation_score_125000\\)) %>%\n  dplyr::mutate(binID = paste(chrom, start, end, sep = \\_\\))\ncolnames(insScore.dTAG) <- c(\\chrom\\, \\start\\, \\end\\, \\insulationScore\\, \\binID\\)\ninsScore.A485 <- fread(here(resultDir, \\TAD\\, \\insulationScore_25kb_G1A485.tsv\\)) %>% \n  dplyr::select(c(\\chrom\\, \\start\\, \\end\\, \\log2_insulation_score_125000\\)) %>%\n  dplyr::mutate(binID = paste(chrom, start, end, sep = \\_\\))\ncolnames(insScore.A485) <- c(\\chrom\\, \\start\\, \\end\\, \\insulationScore\\, \\binID\\)\n\ngetInsulationScore <- function(chr, coordinate, insScore.tb){\n  temp.tb <- insScore.tb %>% dplyr::filter(chrom == chr, start < coordinate, end > coordinate)\n  out <- temp.tb$insulationScore\n  if(length(out) < 1){\n    return(NA)\n  }else{\n    return(temp.tb$insulationScore)\n  }\n}\n\n# Importing TAD boundaries\ntad_boundary <- fread(here(\\../../result/TAD\\, \\TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\\))\ncolnames(tad_boundary) <- c(\\chr\\, \\start\\, \\end\\)\ntad_boundary <- tad_boundary %>% rowwise() %>%\n  dplyr::mutate(tad_id = paste(chr, start, end, sep = \\_\\),\n                center = (start + end)/2,\n                insScore_DMSO = getInsulationScore(chr, center, insScore.DMSO),\n                insScore_dTAG = getInsulationScore(chr, center, insScore.dTAG),\n                insScore_A485 = getInsulationScore(chr, center, insScore.A485))\n\ntad_boundary <- tad_boundary %>% filter(!if_any(everything(), is.na))\n\ntad_boundary$density <- get_density(tad_boundary$insScore_DMSO, tad_boundary$insScore_dTAG, n = 100)\ntad_boundary <- tad_boundary %>% dplyr::arrange(density)\nggplot(tad_boundary, aes(x = insScore_DMSO, y = insScore_dTAG, color = density)) + \n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0) + theme_bw()\n\n\n\ntad_boundary$density <- get_density(tad_boundary$insScore_DMSO, tad_boundary$insScore_A485, n = 100)\ntad_boundary <- tad_boundary %>% dplyr::arrange(density)\nggplot(tad_boundary, aes(x = insScore_DMSO, y = insScore_A485, color = density)) + \n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0) + theme_bw()\n\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
consensus.loop.anno.tb <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import insulation score calculated with python
resultDir <- here(\../../result\)
insScore.DMSO <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1DMSO.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.DMSO) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
insScore.dTAG <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1dTAG.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.dTAG) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
insScore.A485 <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1A485.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.A485) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
getInsulationScore <- function(chr, coordinate, insScore.tb){
temp.tb <- insScore.tb %>% dplyr::filter(chrom == chr, start < coordinate, end > coordinate)
out <- temp.tb$insulationScore
if(length(out) < 1){
return(NA)
}else{
return(temp.tb$insulationScore)
}
}
# Importing TAD boundaries
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% rowwise() %>%
dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
center = (start + end)/2,
insScore_DMSO = getInsulationScore(chr, center, insScore.DMSO),
insScore_dTAG = getInsulationScore(chr, center, insScore.dTAG),
insScore_A485 = getInsulationScore(chr, center, insScore.A485))
tad_boundary <- tad_boundary %>% filter(!if_any(everything(), is.na))
tad_boundary$density <- get_density(tad_boundary$insScore_DMSO, tad_boundary$insScore_dTAG, n = 100)
tad_boundary <- tad_boundary %>% dplyr::arrange(density)
ggplot(tad_boundary, aes(x = insScore_DMSO, y = insScore_dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() + coord_fixed() +
geom_abline(slope = 1, intercept = 0) + theme_bw()
tad_boundary$density <- get_density(tad_boundary$insScore_DMSO, tad_boundary$insScore_A485, n = 100)
tad_boundary <- tad_boundary %>% dplyr::arrange(density)
ggplot(tad_boundary, aes(x = insScore_DMSO, y = insScore_A485, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() + coord_fixed() +
geom_abline(slope = 1, intercept = 0) + theme_bw()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\nconsensus.loop.anno.tb <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n\n# Import insulation score calculated with python\nresultDir <- here(\\../../result\\)\ninsScore.DMSO <- fread(here(resultDir, \\TAD\\, \\insulationScore_25kb_G1DMSO.tsv\\)) %>% \n  dplyr::select(c(\\chrom\\, \\start\\, \\end\\, \\log2_insulation_score_125000\\)) %>%\n  dplyr::mutate(binID = paste(chrom, start, end, sep = \\_\\))\ncolnames(insScore.DMSO) <- c(\\chrom\\, \\start\\, \\end\\, \\insulationScore\\, \\binID\\)\ninsScore.dTAG <- fread(here(resultDir, \\TAD\\, \\insulationScore_25kb_G1dTAG.tsv\\)) %>% \n  dplyr::select(c(\\chrom\\, \\start\\, \\end\\, \\log2_insulation_score_125000\\)) %>%\n  dplyr::mutate(binID = paste(chrom, start, end, sep = \\_\\))\ncolnames(insScore.dTAG) <- c(\\chrom\\, \\start\\, \\end\\, \\insulationScore\\, \\binID\\)\ninsScore.A485 <- fread(here(resultDir, \\TAD\\, \\insulationScore_25kb_G1A485.tsv\\)) %>% \n  dplyr::select(c(\\chrom\\, \\start\\, \\end\\, \\log2_insulation_score_125000\\)) %>%\n  dplyr::mutate(binID = paste(chrom, start, end, sep = \\_\\))\ncolnames(insScore.A485) <- c(\\chrom\\, \\start\\, \\end\\, \\insulationScore\\, \\binID\\)\n\ngetInsulationScore <- function(chr, coordinate, insScore.tb){\n  temp.tb <- insScore.tb %>% dplyr::filter(chrom == chr, start < coordinate, end > coordinate)\n  out <- temp.tb$insulationScore\n  if(length(out) < 1){\n    return(NA)\n  }else{\n    return(temp.tb$insulationScore)\n  }\n}\n\n# Importing TAD boundaries\ntad_boundary <- fread(here(\\../../result/TAD\\, \\TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\\))\ncolnames(tad_boundary) <- c(\\chr\\, \\start\\, \\end\\)\ntad_boundary <- tad_boundary %>% rowwise() %>%\n  dplyr::mutate(tad_id = paste(chr, start, end, sep = \\_\\),\n                center = (start + end)/2,\n                insScore_DMSO = getInsulationScore(chr, center, insScore.DMSO),\n                insScore_dTAG = getInsulationScore(chr, center, insScore.dTAG),\n                insScore_A485 = getInsulationScore(chr, center, insScore.A485))\n\ntad_boundary <- tad_boundary %>% filter(!if_any(everything(), is.na))\n\ntad_boundary$density <- get_density(tad_boundary$insScore_DMSO, tad_boundary$insScore_dTAG, n = 100)\ntad_boundary <- tad_boundary %>% dplyr::arrange(density)\nggplot(tad_boundary, aes(x = insScore_DMSO, y = insScore_dTAG, color = density)) + \n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0) + theme_bw()\n\n\n\ntad_boundary$density <- get_density(tad_boundary$insScore_DMSO, tad_boundary$insScore_A485, n = 100)\ntad_boundary <- tad_boundary %>% dplyr::arrange(density)\nggplot(tad_boundary, aes(x = insScore_DMSO, y = insScore_A485, color = density)) + \n  geom_point(show.legend = FALSE) + \n  scale_color_viridis() + coord_fixed() +\n  geom_abline(slope = 1, intercept = 0) + theme_bw()\n\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
name <- \chromo_cons_annoHierarchy\
consensus.loop.anno.tb <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import insulation score calculated with python
resultDir <- here(\../../result\)
insScore.DMSO <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1DMSO.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.DMSO) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
insScore.dTAG <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1dTAG.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.dTAG) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
insScore.A485 <- fread(here(resultDir, \TAD\, \insulationScore_25kb_G1A485.tsv\)) %>%
dplyr::select(c(\chrom\, \start\, \end\, \log2_insulation_score_125000\)) %>%
dplyr::mutate(binID = paste(chrom, start, end, sep = \_\))
colnames(insScore.A485) <- c(\chrom\, \start\, \end\, \insulationScore\, \binID\)
getInsulationScore <- function(chr, coordinate, insScore.tb){
temp.tb <- insScore.tb %>% dplyr::filter(chrom == chr, start < coordinate, end > coordinate)
out <- temp.tb$insulationScore
if(length(out) < 1){
return(NA)
}else{
return(temp.tb$insulationScore)
}
}
# Importing TAD boundaries
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% rowwise() %>%
dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
center = (start + end)/2,
insScore_DMSO = getInsulationScore(chr, center, insScore.DMSO),
insScore_dTAG = getInsulationScore(chr, center, insScore.dTAG),
insScore_A485 = getInsulationScore(chr, center, insScore.A485))
tad_boundary <- tad_boundary %>% filter(!if_any(everything(), is.na))
tad_boundary$density <- get_density(tad_boundary$insScore_DMSO, tad_boundary$insScore_dTAG, n = 100)
tad_boundary <- tad_boundary %>% dplyr::arrange(density)
ggplot(tad_boundary, aes(x = insScore_DMSO, y = insScore_dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() + coord_fixed() +
geom_abline(slope = 1, intercept = 0) + theme_bw()
tad_boundary$density <- get_density(tad_boundary$insScore_DMSO, tad_boundary$insScore_A485, n = 100)
tad_boundary <- tad_boundary %>% dplyr::arrange(density)
ggplot(tad_boundary, aes(x = insScore_DMSO, y = insScore_A485, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() + coord_fixed() +
geom_abline(slope = 1, intercept = 0) + theme_bw()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Checking TAD average log2FC
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n### Importing differential regulatory loops\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(group = \\UP/NO\\)\ncolnames(loop.up) <- c(\\chr1\\, \\start1\\, \\end1\\, \\chr2\\, \\start2\\, \\end2\\, \\group\\)\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(group = \\UP/NO\\)\ncolnames(loop.no) <- c(\\chr1\\, \\start1\\, \\end1\\, \\chr2\\, \\start2\\, \\end2\\, \\group\\)\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(group = \\DOWN\\)\ncolnames(loop.down) <- c(\\chr1\\, \\start1\\, \\end1\\, \\chr2\\, \\start2\\, \\end2\\, \\group\\)\n\nloops <- bind_rows(bind_rows(loop.up, loop.no), loop.down)\n\n### Importing TAD boundary\ntad_boundary <- fread(here(\\../../result/TAD\\, \\TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\\))\ncolnames(tad_boundary) <- c(\\chr\\, \\start\\, \\end\\)\ntad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \\_\\),\n                                               start = start -175000/2,\n                                               end = end + 175000/2,\n                                               center = (start + end)/2)\n### Functions\ncheckBoundaryCross <- function(chrom1, start1, end2, tad_boundary){\n  temp <- tad_boundary %>% dplyr::filter(chr == chrom1,\n                                 center > start1,\n                                 center < end2)\n  return(length(temp$center))\n}\n\n### Check overlap\nloops <- loops %>% rowwise() %>%\n  dplyr::mutate(boundaryCross = checkBoundaryCross(chr1, start1, end2, tad_boundary))\n\n### Plotting\nsummary_data <- loops %>%\n  group_by(group) %>%\n  summarise(percentage = mean(boundaryCross > 0) * 100)\n\nsummary_data$group <- factor(summary_data$group, levels = c(\\UP/NO\\, \\DOWN\\))\n\np <- ggplot(summary_data, aes(x = group, y = percentage)) +\n  geom_bar(stat = \\identity\\, fill = darken(strong_green, amount = 0.2)) +\n  labs(y = \\% of loops crossing TAD boundary\\, x = NULL) +\n  theme_classic() + ylim(0, 100) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n  )\n\nfileName <- paste0(\\boundaryCrossRatio\\)\nwidth <- panelSize(1)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
### Importing differential regulatory loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \UP/NO\)
colnames(loop.up) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \UP/NO\)
colnames(loop.no) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \DOWN\)
colnames(loop.down) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loops <- bind_rows(bind_rows(loop.up, loop.no), loop.down)
### Importing TAD boundary
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
start = start -175000/2,
end = end + 175000/2,
center = (start + end)/2)
### Functions
checkBoundaryCross <- function(chrom1, start1, end2, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom1,
center > start1,
center < end2)
return(length(temp$center))
}
### Check overlap
loops <- loops %>% rowwise() %>%
dplyr::mutate(boundaryCross = checkBoundaryCross(chr1, start1, end2, tad_boundary))
### Plotting
summary_data <- loops %>%
group_by(group) %>%
summarise(percentage = mean(boundaryCross > 0) * 100)
summary_data$group <- factor(summary_data$group, levels = c(\UP/NO\, \DOWN\))
p <- ggplot(summary_data, aes(x = group, y = percentage)) +
geom_bar(stat = \identity\, fill = darken(strong_green, amount = 0.2)) +
labs(y = \% of loops crossing TAD boundary\, x = NULL) +
theme_classic() + ylim(0, 100) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
)
fileName <- paste0(\boundaryCrossRatio\)
width <- panelSize(1)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n### Importing differential regulatory loops\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(group = \\UP/NO\\)\ncolnames(loop.up) <- c(\\chr1\\, \\start1\\, \\end1\\, \\chr2\\, \\start2\\, \\end2\\, \\group\\)\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(group = \\UP/NO\\)\ncolnames(loop.no) <- c(\\chr1\\, \\start1\\, \\end1\\, \\chr2\\, \\start2\\, \\end2\\, \\group\\)\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(group = \\DOWN\\)\ncolnames(loop.down) <- c(\\chr1\\, \\start1\\, \\end1\\, \\chr2\\, \\start2\\, \\end2\\, \\group\\)\n\nloops <- bind_rows(bind_rows(loop.up, loop.no), loop.down)\n\n### Importing TAD boundary\ntad_boundary <- fread(here(\\../../result/TAD\\, \\TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\\))\ncolnames(tad_boundary) <- c(\\chr\\, \\start\\, \\end\\)\ntad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \\_\\),\n                                               start = start -175000/2,\n                                               end = end + 175000/2,\n                                               center = (start + end)/2)\n### Functions\ncheckBoundaryCross <- function(chrom1, start1, end2, tad_boundary){\n  temp <- tad_boundary %>% dplyr::filter(chr == chrom1,\n                                 center > start1,\n                                 center < end2)\n  return(length(temp$center))\n}\n\n### Check overlap\nloops <- loops %>% rowwise() %>%\n  dplyr::mutate(boundaryCross = checkBoundaryCross(chr1, start1, end2, tad_boundary))\n\n### Plotting\nsummary_data <- loops %>%\n  group_by(group) %>%\n  summarise(percentage = mean(boundaryCross > 0) * 100)\n\nsummary_data$group <- factor(summary_data$group, levels = c(\\UP/NO\\, \\DOWN\\))\n\np <- ggplot(summary_data, aes(x = group, y = percentage)) +\n  geom_bar(stat = \\identity\\, fill = darken(strong_green, amount = 0.2)) +\n  labs(y = \\% of loops crossing TAD boundary\\, x = NULL) +\n  theme_classic() + ylim(0, 100) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n  )\n\nfileName <- paste0(\\boundaryCrossRatio\\)\nwidth <- panelSize(1)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
### Importing differential regulatory loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \UP/NO\)
colnames(loop.up) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \UP/NO\)
colnames(loop.no) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \DOWN\)
colnames(loop.down) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loops <- bind_rows(bind_rows(loop.up, loop.no), loop.down)
### Importing TAD boundary
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
start = start -175000/2,
end = end + 175000/2,
center = (start + end)/2)
### Functions
checkBoundaryCross <- function(chrom1, start1, end2, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom1,
center > start1,
center < end2)
return(length(temp$center))
}
### Check overlap
loops <- loops %>% rowwise() %>%
dplyr::mutate(boundaryCross = checkBoundaryCross(chr1, start1, end2, tad_boundary))
### Plotting
summary_data <- loops %>%
group_by(group) %>%
summarise(percentage = mean(boundaryCross > 0) * 100)
summary_data$group <- factor(summary_data$group, levels = c(\UP/NO\, \DOWN\))
p <- ggplot(summary_data, aes(x = group, y = percentage)) +
geom_bar(stat = \identity\, fill = darken(strong_green, amount = 0.2)) +
labs(y = \% of loops crossing TAD boundary\, x = NULL) +
theme_classic() + ylim(0, 100) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
)
fileName <- paste0(\boundaryCrossRatio\)
width <- panelSize(1)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.18] Comparing TAD insulation score across samples
Checking how insulation score changes by treatment at DMSO TAD boundaries
<!-- rnb-text-end -->
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### Importing differential regulatory loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \up\)
colnames(loop.up) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \no\)
colnames(loop.no) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \down\)
colnames(loop.down) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loops <- bind_rows(bind_rows(loop.up, loop.no), loop.down)
### Importing TAD boundary
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
start = start -175000/2,
end = end + 175000/2,
center = (start + end)/2)
### Functions
checkBoundaryCross <- function(chrom1, start1, end2, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom1,
center > start1,
center < end2)
return(length(temp$center))
}
### Check overlap
loops <- loops %>% rowwise() %>%
dplyr::mutate(boundaryCross = checkBoundaryCross(chr1, start1, end2, tad_boundary))
### Plotting
summary_data <- loops %>%
group_by(group) %>%
summarise(percentage = mean(boundaryCross > 0) * 100)
p <- ggplot(summary_data, aes(x = group, y = percentage)) +
geom_bar(stat = \identity\, fill = \skyblue\) +
labs(title = \Percentage of Rows with boundaryCross > 0 per Group\,
x = \Group\,
y = \Percentage\) +
theme_classic() + ylim(0, 100)
fileName <- paste0(\boundaryCrossRatio_A485\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->
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```r
```r
### Importing differential regulatory loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \up\)
colnames(loop.up) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \no\)
colnames(loop.no) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(group = \down\)
colnames(loop.down) <- c(\chr1\, \start1\, \end1\, \chr2\, \start2\, \end2\, \group\)
loops <- bind_rows(bind_rows(loop.up, loop.no), loop.down)
### Importing TAD boundary
tad_boundary <- fread(here(\../../result/TAD\, \TAD_25kb_125kb_otsu_boundaries_G1DMSO.bed\))
colnames(tad_boundary) <- c(\chr\, \start\, \end\)
tad_boundary <- tad_boundary %>% dplyr::mutate(tad_id = paste(chr, start, end, sep = \_\),
start = start -175000/2,
end = end + 175000/2,
center = (start + end)/2)
### Functions
checkBoundaryCross <- function(chrom1, start1, end2, tad_boundary){
temp <- tad_boundary %>% dplyr::filter(chr == chrom1,
center > start1,
center < end2)
return(length(temp$center))
}
### Check overlap
loops <- loops %>% rowwise() %>%
dplyr::mutate(boundaryCross = checkBoundaryCross(chr1, start1, end2, tad_boundary))
### Plotting
summary_data <- loops %>%
group_by(group) %>%
summarise(percentage = mean(boundaryCross > 0) * 100)
p <- ggplot(summary_data, aes(x = group, y = percentage)) +
geom_bar(stat = \identity\, fill = \skyblue\) +
labs(title = \Percentage of Rows with boundaryCross > 0 per Group\,
x = \Group\,
y = \Percentage\) +
theme_classic() + ylim(0, 100)
fileName <- paste0(\boundaryCrossRatio_A485\)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.20] Checking how many loops cross the boundary?
Previously, I tried to check the percentage of loops crossing the boundary per group which wasn't fruitful.
This time, try to do this on the differential loops.
#### dTAG
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
# LOADING LOLA DB
library(\simpleCache\)
library(\LOLA\)
lolaDB = loadRegionDB(\/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/reference/LOLACore_cached/mm10\)
# FUNCTIONS
extractAnchor <- function(loop){
anchor1 <- loop %>% dplyr::select(c(1, 2, 3))
colnames(anchor1) <- c(\chr\, \start\, \end\)
anchor2 <- loop %>% dplyr::select(c(4, 5, 6))
colnames(anchor2) <- c(\chr\, \start\, \end\)
anchors <- reduce(makeGRangesFromDataFrame(bind_rows(anchor1, anchor2)))
return(anchors)
}
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
# LOADING LOLA DB
library(\simpleCache\)
library(\LOLA\)
lolaDB = loadRegionDB(\/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/reference/LOLACore_cached/mm10\)
# FUNCTIONS
extractAnchor <- function(loop){
anchor1 <- loop %>% dplyr::select(c(1, 2, 3))
colnames(anchor1) <- c(\chr\, \start\, \end\)
anchor2 <- loop %>% dplyr::select(c(4, 5, 6))
colnames(anchor2) <- c(\chr\, \start\, \end\)
anchors <- reduce(makeGRangesFromDataFrame(bind_rows(anchor1, anchor2)))
return(anchors)
}
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### A485
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\natac <- fread(here(refDir, \\GSM3106257_ATAC_ESC_1.bed\\)) %>% dplyr::select(V1, V2, V3)\ncolnames(atac) <- c(\\chr\\, \\start\\, \\end\\)\natac.gr <- makeGRangesFromDataFrame(atac)\n\n# LOADING LOOPS\n### Importing differential regulatory loops & extract anchor\nloop.all <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\n\nloop.1 <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\\))\nanchor.1 <- (extractAnchor(loop.1))\noverlaps <- findOverlaps(anchor.1, atac.gr)\nanchor.1 <- pintersect(anchor.1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.2 <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\\))\nanchor.2 <- (extractAnchor(loop.2))\noverlaps <- findOverlaps(anchor.2, atac.gr)\nanchor.2 <- pintersect(anchor.2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.3 <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\\))\nanchor.3 <- (extractAnchor(loop.3))\noverlaps <- findOverlaps(anchor.3, atac.gr)\nanchor.3 <- pintersect(anchor.3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.4 <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\\))\nanchor.4 <- (extractAnchor(loop.4))\noverlaps <- findOverlaps(anchor.4, atac.gr)\nanchor.4 <- pintersect(anchor.4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\nanchors <- list(anchor.1, anchor.2, anchor.3, anchor.4)\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  \n  anchor <- anchors[[i]]\n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ncolnames(temp) <- c(\\target\\, \\bothRetained\\, \\AsyncSpecificPerturb\\, \\G1SpecificPerturb\\, \\bothPerturb\\)\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.1 <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\))
anchor.1 <- (extractAnchor(loop.1))
overlaps <- findOverlaps(anchor.1, atac.gr)
anchor.1 <- pintersect(anchor.1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.2 <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\))
anchor.2 <- (extractAnchor(loop.2))
overlaps <- findOverlaps(anchor.2, atac.gr)
anchor.2 <- pintersect(anchor.2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.3 <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\))
anchor.3 <- (extractAnchor(loop.3))
overlaps <- findOverlaps(anchor.3, atac.gr)
anchor.3 <- pintersect(anchor.3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.4 <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\))
anchor.4 <- (extractAnchor(loop.4))
overlaps <- findOverlaps(anchor.4, atac.gr)
anchor.4 <- pintersect(anchor.4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchors <- list(anchor.1, anchor.2, anchor.3, anchor.4)
tbs <- list()
temps <- list()
# Process clusters c1 to c8
for (i in 1:4) {
anchor <- anchors[[i]]
# Run LOLA
result <- runLOLA(anchor, anchor.all, lolaDB)
tb <- as_tibble(result)
# Filter and summarize
tb <- tb %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
# Store tb
tbs[[i]] <- tb
# Select and rename oddsRatio
temp <- tb %>% dplyr::select(target, oddsRatio)
colnames(temp) <- c(\target\, paste0(\OR_c\, i))
# Store temp
temps[[i]] <- temp
}
# Merge all temp tables into one
temp <- Reduce(function(x, y) full_join(x, y, by = \target\), temps) %>%
mutate_all(~replace_na(., 1))
colnames(temp) <- c(\target\, \bothRetained\, \AsyncSpecificPerturb\, \G1SpecificPerturb\, \bothPerturb\)
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\natac <- fread(here(refDir, \\GSM3106257_ATAC_ESC_1.bed\\)) %>% dplyr::select(V1, V2, V3)\ncolnames(atac) <- c(\\chr\\, \\start\\, \\end\\)\natac.gr <- makeGRangesFromDataFrame(atac)\n\n# LOADING LOOPS\n### Importing differential regulatory loops & extract anchor\nloop.all <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\n\nloop.1 <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\\))\nanchor.1 <- (extractAnchor(loop.1))\noverlaps <- findOverlaps(anchor.1, atac.gr)\nanchor.1 <- pintersect(anchor.1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.2 <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\\))\nanchor.2 <- (extractAnchor(loop.2))\noverlaps <- findOverlaps(anchor.2, atac.gr)\nanchor.2 <- pintersect(anchor.2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.3 <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\\))\nanchor.3 <- (extractAnchor(loop.3))\noverlaps <- findOverlaps(anchor.3, atac.gr)\nanchor.3 <- pintersect(anchor.3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nloop.4 <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\\))\nanchor.4 <- (extractAnchor(loop.4))\noverlaps <- findOverlaps(anchor.4, atac.gr)\nanchor.4 <- pintersect(anchor.4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\nanchors <- list(anchor.1, anchor.2, anchor.3, anchor.4)\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  \n  anchor <- anchors[[i]]\n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ncolnames(temp) <- c(\\target\\, \\bothRetained\\, \\AsyncSpecificPerturb\\, \\G1SpecificPerturb\\, \\bothPerturb\\)\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.1 <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\))
anchor.1 <- (extractAnchor(loop.1))
overlaps <- findOverlaps(anchor.1, atac.gr)
anchor.1 <- pintersect(anchor.1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.2 <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\))
anchor.2 <- (extractAnchor(loop.2))
overlaps <- findOverlaps(anchor.2, atac.gr)
anchor.2 <- pintersect(anchor.2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.3 <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\))
anchor.3 <- (extractAnchor(loop.3))
overlaps <- findOverlaps(anchor.3, atac.gr)
anchor.3 <- pintersect(anchor.3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.4 <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\))
anchor.4 <- (extractAnchor(loop.4))
overlaps <- findOverlaps(anchor.4, atac.gr)
anchor.4 <- pintersect(anchor.4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchors <- list(anchor.1, anchor.2, anchor.3, anchor.4)
tbs <- list()
temps <- list()
# Process clusters c1 to c8
for (i in 1:4) {
anchor <- anchors[[i]]
# Run LOLA
result <- runLOLA(anchor, anchor.all, lolaDB)
tb <- as_tibble(result)
# Filter and summarize
tb <- tb %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
# Store tb
tbs[[i]] <- tb
# Select and rename oddsRatio
temp <- tb %>% dplyr::select(target, oddsRatio)
colnames(temp) <- c(\target\, paste0(\OR_c\, i))
# Store temp
temps[[i]] <- temp
}
# Merge all temp tables into one
temp <- Reduce(function(x, y) full_join(x, y, by = \target\), temps) %>%
mutate_all(~replace_na(., 1))
colnames(temp) <- c(\target\, \bothRetained\, \AsyncSpecificPerturb\, \G1SpecificPerturb\, \bothPerturb\)
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
```
```
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## [2.21] LOLA on differential loop anchors
##### LOADING LOLA
<!-- rnb-text-end -->
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<!-- rnb-output-begin 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LjIuYmVkcGVcXCkpXG5hbmNob3IuZG93biA8LSAoZXh0cmFjdEFuY2hvcihsb29wLmRvd24pKVxub3ZlcmxhcHMgPC0gZmluZE92ZXJsYXBzKGFuY2hvci5kb3duLCBhdGFjLmdyKVxuYW5jaG9yLmRvd24gPC0gcGludGVyc2VjdChhbmNob3IuZG93bltxdWVyeUhpdHMob3ZlcmxhcHMpXSwgYXRhYy5ncltzdWJqZWN0SGl0cyhvdmVybGFwcyldKVxuXG5cblxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.upno <- bind_rows(loop.up, loop.no)
anchor.upno <- (extractAnchor(loop.upno))
overlaps <- findOverlaps(anchor.upno, atac.gr)
anchor.upno <- pintersect(anchor.upno[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
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```r
```r
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.upno <- bind_rows(loop.up, loop.no)
anchor.upno <- (extractAnchor(loop.upno))
overlaps <- findOverlaps(anchor.upno, atac.gr)
anchor.upno <- pintersect(anchor.upno[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
```
```
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#### dTAG, Comparing to Async
##### - Limited to ATAC-seq signal, reg loop backgroupd
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 -->\n\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\), sep = \\\\t\\)\n\n# UPNO\nresult = runLOLA(anchor.upno, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\), sep = \\\\t\\)\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\n# p <- Heatmap(\n#   data,\n#   name = \\Odds Ratio\\,                   # Name of the heatmap legend\n#   cluster_columns = FALSE,            # Remove column dendrogram\n#   row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n#   show_row_dend = FALSE,\n#   col = col_fun                       # Use the red gradient color scale\n# )\n# \n# fileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_regAnchorBackground_atac\\)\n# height <- 7\n# width <- 3.5\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n# \n\n\n### Visualizing p-value and OR\nalpha <- 0.05\n# tb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\UP\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\NO\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n\ntb.upno <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\UP/NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.upno %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac_pub.tsv\\), sep = \\\\t\\)\n\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.down %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac_pub.tsv\\), sep = \\\\t\\)\n\n\ntemp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.upno, temp.down)\n\n# order <- c((temp %>% dplyr::filter(group == \\UP/NO\\) %>% arrange(desc(oddsRatio)))$target, \n#            (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\n\n# temp$target <- factor(temp$target, levels = rev(order))\ntemp$group <- factor(temp$group, levels = c(\\UP/NO\\, \\DOWN\\))\n\ntargetList <- c(\\POLR2A\\, \\CTR9\\,\n                \\AFF4\\, \\ELL2\\,\n                \\MED1\\, \\MED12\\,\n                \\TBP\\, \\TAF1\\, \\TAF3\\,\n                \\E2F1\\, \\YY1\\, \\NIPBL\\, \n                \\EP300\\, \\DPY30\\, \\SETDB1\\,\n                \\RAD21\\, \\SMC1A\\, \\SMC3\\, \\CTCF\\,\n                \\SUZ12\\, \\PHF19\\\n)\ntemp <- temp %>% dplyr::filter(target %in% targetList)\ntemp$target <- factor(temp$target, levels = rev(targetList))\n\n\nqValueLogMax <- 50\ntemp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))\n\np <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n#   geom_point() + theme_bw() + \n#   scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n#   scale_color_gradient(low = \\blue\\, high = \\red\\,\n#                        guide = guide_colorbar(\n#                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n#                          barheight = 15/5.08    # Adjust height of the color bar\n#                        )) +\n#   labs(x = NULL, y = NULL)  +\n#   theme(\n#     axis.title = element_text(\n#       size = fontSizeS,\n#       family = fontType,\n#       color = \\#000000\\\n#     ),\n#     axis.text = element_text(\n#       size = fontSizeS,\n#       family = fontType,\n#       color = \\#000000\\\n#     ),\n#     axis.text.x = element_text(\n#       angle = 45,      # Rotate x-axis labels 45 degrees\n#       hjust = 1,       # Adjust horizontal justification\n#       vjust = 1        # Adjust vertical justification\n#     ),\n#     axis.line = element_line(\n#       color = \\#000000\\,\n#       size = lineThick*mmToLineUnit,\n#       lineend = \\square\\\n#     ),\n#     axis.ticks = element_line(\n#       color = \\#000000\\,\n#       size = lineThick*mmToLineUnit,\n#       lineend = \\square\\\n#     ),\n#     panel.background = element_rect(fill = \\transparent\\),\n#     legend.text = element_text(family = fontType, size = fontSizeS),\n#     legend.title = element_text(family = fontType, size = fontSizeS)\n#   )\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac_upno\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(1.9)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\), sep = \\t\)
# UPNO
result = runLOLA(anchor.upno, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
# p <- Heatmap(
# data,
# name = \Odds Ratio\, # Name of the heatmap legend
# cluster_columns = FALSE, # Remove column dendrogram
# row_km = 4, # Define the number of k-means clusters for rows (adjust as needed)
# show_row_dend = FALSE,
# col = col_fun # Use the red gradient color scale
# )
#
# fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_regAnchorBackground_atac\)
# height <- 7
# width <- 3.5
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
#
### Visualizing p-value and OR
alpha <- 0.05
# tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \UP\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
# tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \NO\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
tb.upno <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = \UP/NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.upno %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_atac_pub.tsv\), sep = \\t\)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.down %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac_pub.tsv\), sep = \\t\)
temp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.upno, temp.down)
# order <- c((temp %>% dplyr::filter(group == \UP/NO\) %>% arrange(desc(oddsRatio)))$target,
# (temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
# temp$target <- factor(temp$target, levels = rev(order))
temp$group <- factor(temp$group, levels = c(\UP/NO\, \DOWN\))
targetList <- c(\POLR2A\, \CTR9\,
\AFF4\, \ELL2\,
\MED1\, \MED12\,
\TBP\, \TAF1\, \TAF3\,
\E2F1\, \YY1\, \NIPBL\,
\EP300\, \DPY30\, \SETDB1\,
\RAD21\, \SMC1A\, \SMC3\, \CTCF\,
\SUZ12\, \PHF19\
)
temp <- temp %>% dplyr::filter(target %in% targetList)
temp$target <- factor(temp$target, levels = rev(targetList))
qValueLogMax <- 50
temp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))
p <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
# geom_point() + theme_bw() +
# scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
# scale_color_gradient(low = \blue\, high = \red\,
# guide = guide_colorbar(
# barwidth = 1.5/5.08, # Adjust width of the color bar
# barheight = 15/5.08 # Adjust height of the color bar
# )) +
# labs(x = NULL, y = NULL) +
# theme(
# axis.title = element_text(
# size = fontSizeS,
# family = fontType,
# color = \#000000\
# ),
# axis.text = element_text(
# size = fontSizeS,
# family = fontType,
# color = \#000000\
# ),
# axis.text.x = element_text(
# angle = 45, # Rotate x-axis labels 45 degrees
# hjust = 1, # Adjust horizontal justification
# vjust = 1 # Adjust vertical justification
# ),
# axis.line = element_line(
# color = \#000000\,
# size = lineThick*mmToLineUnit,
# lineend = \square\
# ),
# axis.ticks = element_line(
# color = \#000000\,
# size = lineThick*mmToLineUnit,
# lineend = \square\
# ),
# panel.background = element_rect(fill = \transparent\),
# legend.text = element_text(family = fontType, size = fontSizeS),
# legend.title = element_text(family = fontType, size = fontSizeS)
# )
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac_upno\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.9)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\), sep = \\\\t\\)\n\n# UPNO\nresult = runLOLA(anchor.upno, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\), sep = \\\\t\\)\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\n# p <- Heatmap(\n#   data,\n#   name = \\Odds Ratio\\,                   # Name of the heatmap legend\n#   cluster_columns = FALSE,            # Remove column dendrogram\n#   row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n#   show_row_dend = FALSE,\n#   col = col_fun                       # Use the red gradient color scale\n# )\n# \n# fileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_regAnchorBackground_atac\\)\n# height <- 7\n# width <- 3.5\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n# \n\n\n### Visualizing p-value and OR\nalpha <- 0.05\n# tb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\UP\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\NO\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n\ntb.upno <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\UP/NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.upno %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac_pub.tsv\\), sep = \\\\t\\)\n\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.down %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac_pub.tsv\\), sep = \\\\t\\)\n\n\ntemp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.upno, temp.down)\n\n# order <- c((temp %>% dplyr::filter(group == \\UP/NO\\) %>% arrange(desc(oddsRatio)))$target, \n#            (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\n\n# temp$target <- factor(temp$target, levels = rev(order))\ntemp$group <- factor(temp$group, levels = c(\\UP/NO\\, \\DOWN\\))\n\ntargetList <- c(\\POLR2A\\, \\CTR9\\,\n                \\AFF4\\, \\ELL2\\,\n                \\MED1\\, \\MED12\\,\n                \\TBP\\, \\TAF1\\, \\TAF3\\,\n                \\E2F1\\, \\YY1\\, \\NIPBL\\, \n                \\EP300\\, \\DPY30\\, \\SETDB1\\,\n                \\RAD21\\, \\SMC1A\\, \\SMC3\\, \\CTCF\\,\n                \\SUZ12\\, \\PHF19\\\n)\ntemp <- temp %>% dplyr::filter(target %in% targetList)\ntemp$target <- factor(temp$target, levels = rev(targetList))\n\n\nqValueLogMax <- 50\ntemp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))\n\np <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n#   geom_point() + theme_bw() + \n#   scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n#   scale_color_gradient(low = \\blue\\, high = \\red\\,\n#                        guide = guide_colorbar(\n#                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n#                          barheight = 15/5.08    # Adjust height of the color bar\n#                        )) +\n#   labs(x = NULL, y = NULL)  +\n#   theme(\n#     axis.title = element_text(\n#       size = fontSizeS,\n#       family = fontType,\n#       color = \\#000000\\\n#     ),\n#     axis.text = element_text(\n#       size = fontSizeS,\n#       family = fontType,\n#       color = \\#000000\\\n#     ),\n#     axis.text.x = element_text(\n#       angle = 45,      # Rotate x-axis labels 45 degrees\n#       hjust = 1,       # Adjust horizontal justification\n#       vjust = 1        # Adjust vertical justification\n#     ),\n#     axis.line = element_line(\n#       color = \\#000000\\,\n#       size = lineThick*mmToLineUnit,\n#       lineend = \\square\\\n#     ),\n#     axis.ticks = element_line(\n#       color = \\#000000\\,\n#       size = lineThick*mmToLineUnit,\n#       lineend = \\square\\\n#     ),\n#     panel.background = element_rect(fill = \\transparent\\),\n#     legend.text = element_text(family = fontType, size = fontSizeS),\n#     legend.title = element_text(family = fontType, size = fontSizeS)\n#   )\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac_upno\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(1.9)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\), sep = \\\\t\\)\n\n# UPNO\nresult = runLOLA(anchor.upno, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\), sep = \\\\t\\)\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\n# p <- Heatmap(\n#   data,\n#   name = \\Odds Ratio\\,                   # Name of the heatmap legend\n#   cluster_columns = FALSE,            # Remove column dendrogram\n#   row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n#   show_row_dend = FALSE,\n#   col = col_fun                       # Use the red gradient color scale\n# )\n# \n# fileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_regAnchorBackground_atac\\)\n# height <- 7\n# width <- 3.5\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n# \n\n\n### Visualizing p-value and OR\nalpha <- 0.05\n# tb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\UP\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\NO\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n\ntb.upno <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\UP/NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.upno %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_atac_pub.tsv\\), sep = \\\\t\\)\n\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.down %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac_pub.tsv\\), sep = \\\\t\\)\n\n\ntemp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.upno, temp.down)\n\n# order <- c((temp %>% dplyr::filter(group == \\UP/NO\\) %>% arrange(desc(oddsRatio)))$target, \n#            (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\n\n# temp$target <- factor(temp$target, levels = rev(order))\ntemp$group <- factor(temp$group, levels = c(\\UP/NO\\, \\DOWN\\))\n\ntargetList <- c(\\POLR2A\\, \\CTR9\\,\n                \\AFF4\\, \\ELL2\\,\n                \\MED1\\, \\MED12\\,\n                \\TBP\\, \\TAF1\\, \\TAF3\\,\n                \\E2F1\\, \\YY1\\, \\NIPBL\\, \n                \\EP300\\, \\DPY30\\, \\SETDB1\\,\n                \\RAD21\\, \\SMC1A\\, \\SMC3\\, \\CTCF\\,\n                \\SUZ12\\, \\PHF19\\\n)\ntemp <- temp %>% dplyr::filter(target %in% targetList)\ntemp$target <- factor(temp$target, levels = rev(targetList))\n\n\nqValueLogMax <- 50\ntemp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))\n\np <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n#   geom_point() + theme_bw() + \n#   scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n#   scale_color_gradient(low = \\blue\\, high = \\red\\,\n#                        guide = guide_colorbar(\n#                          barwidth = 1.5/5.08,  # Adjust width of the color bar\n#                          barheight = 15/5.08    # Adjust height of the color bar\n#                        )) +\n#   labs(x = NULL, y = NULL)  +\n#   theme(\n#     axis.title = element_text(\n#       size = fontSizeS,\n#       family = fontType,\n#       color = \\#000000\\\n#     ),\n#     axis.text = element_text(\n#       size = fontSizeS,\n#       family = fontType,\n#       color = \\#000000\\\n#     ),\n#     axis.text.x = element_text(\n#       angle = 45,      # Rotate x-axis labels 45 degrees\n#       hjust = 1,       # Adjust horizontal justification\n#       vjust = 1        # Adjust vertical justification\n#     ),\n#     axis.line = element_line(\n#       color = \\#000000\\,\n#       size = lineThick*mmToLineUnit,\n#       lineend = \\square\\\n#     ),\n#     axis.ticks = element_line(\n#       color = \\#000000\\,\n#       size = lineThick*mmToLineUnit,\n#       lineend = \\square\\\n#     ),\n#     panel.background = element_rect(fill = \\transparent\\),\n#     legend.text = element_text(family = fontType, size = fontSizeS),\n#     legend.title = element_text(family = fontType, size = fontSizeS)\n#   )\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac_upno\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(1.9)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\), sep = \\t\)
# UPNO
result = runLOLA(anchor.upno, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
# p <- Heatmap(
# data,
# name = \Odds Ratio\, # Name of the heatmap legend
# cluster_columns = FALSE, # Remove column dendrogram
# row_km = 4, # Define the number of k-means clusters for rows (adjust as needed)
# show_row_dend = FALSE,
# col = col_fun # Use the red gradient color scale
# )
#
# fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_regAnchorBackground_atac\)
# height <- 7
# width <- 3.5
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
#
### Visualizing p-value and OR
alpha <- 0.05
# tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \UP\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
# tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \NO\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
tb.upno <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = \UP/NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.upno %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_atac_pub.tsv\), sep = \\t\)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.down %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac_pub.tsv\), sep = \\t\)
temp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.upno, temp.down)
# order <- c((temp %>% dplyr::filter(group == \UP/NO\) %>% arrange(desc(oddsRatio)))$target,
# (temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
# temp$target <- factor(temp$target, levels = rev(order))
temp$group <- factor(temp$group, levels = c(\UP/NO\, \DOWN\))
targetList <- c(\POLR2A\, \CTR9\,
\AFF4\, \ELL2\,
\MED1\, \MED12\,
\TBP\, \TAF1\, \TAF3\,
\E2F1\, \YY1\, \NIPBL\,
\EP300\, \DPY30\, \SETDB1\,
\RAD21\, \SMC1A\, \SMC3\, \CTCF\,
\SUZ12\, \PHF19\
)
temp <- temp %>% dplyr::filter(target %in% targetList)
temp$target <- factor(temp$target, levels = rev(targetList))
qValueLogMax <- 50
temp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))
p <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
# geom_point() + theme_bw() +
# scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
# scale_color_gradient(low = \blue\, high = \red\,
# guide = guide_colorbar(
# barwidth = 1.5/5.08, # Adjust width of the color bar
# barheight = 15/5.08 # Adjust height of the color bar
# )) +
# labs(x = NULL, y = NULL) +
# theme(
# axis.title = element_text(
# size = fontSizeS,
# family = fontType,
# color = \#000000\
# ),
# axis.text = element_text(
# size = fontSizeS,
# family = fontType,
# color = \#000000\
# ),
# axis.text.x = element_text(
# angle = 45, # Rotate x-axis labels 45 degrees
# hjust = 1, # Adjust horizontal justification
# vjust = 1 # Adjust vertical justification
# ),
# axis.line = element_line(
# color = \#000000\,
# size = lineThick*mmToLineUnit,
# lineend = \square\
# ),
# axis.ticks = element_line(
# color = \#000000\,
# size = lineThick*mmToLineUnit,
# lineend = \square\
# ),
# panel.background = element_rect(fill = \transparent\),
# legend.text = element_text(family = fontType, size = fontSizeS),
# legend.title = element_text(family = fontType, size = fontSizeS)
# )
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac_upno\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.9)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### dTAG
##### - Limiting to ATAC-seq signal, reg loop background
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin {"data":"```r\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\"target\", \"OR_c1\")\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\"target\", \"OR_c2\")\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\"target\", \"OR_c3\")\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\"target\", \"OR_c4\")\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\"target\", \"OR_c5\")\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\"target\", \"OR_c6\")\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\"target\", \"OR_c7\")\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\"target\", \"OR_c8\")\n\ntemp <- full_join(temp.c1, temp.c2, by = \"target\") %>%\n  full_join(temp.c3, by = \"target\") %>%\n  full_join(temp.c4, by = \"target\") %>%\n  full_join(temp.c5, by = \"target\") %>%\n  full_join(temp.c6, by = \"target\") %>%\n  full_join(temp.c7, by = \"target\") %>%\n  full_join(temp.c8, by = \"target\") %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\"target\") %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\"white\", \"red\"))\n\n\n#fviz_nbclust(data, kmeans, method = \"wss\")\n\np <- Heatmap(\n  data,\n  name = \"Odds Ratio\",                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\"loop.cluster\", i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\"target\", paste0(\"OR_c\", i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \"target\"), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\"target\") %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\"white\", \"red\"))\n\n\n#fviz_nbclust(data, kmeans, method = \"wss\")\n\np <- Heatmap(\n  data,\n  name = \"Odds Ratio\",                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```"} -->\n\n```r\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\\target\\, \\OR_c1\\)\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\\target\\, \\OR_c2\\)\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\\target\\, \\OR_c3\\)\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\\target\\, \\OR_c4\\)\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\\target\\, \\OR_c5\\)\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\\target\\, \\OR_c6\\)\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\\target\\, \\OR_c7\\)\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\\target\\, \\OR_c8\\)\n\ntemp <- full_join(temp.c1, temp.c2, by = \\target\\) %>%\n  full_join(temp.c3, by = \\target\\) %>%\n  full_join(temp.c4, by = \\target\\) %>%\n  full_join(temp.c5, by = \\target\\) %>%\n  full_join(temp.c6, by = \\target\\) %>%\n  full_join(temp.c7, by = \\target\\) %>%\n  full_join(temp.c8, by = \\target\\) %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\\loop.cluster\\, i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n\n<!-- rnb-source-end -->\n"} -->
### 241015 Testing on subset of loops
anchor.c1 <- extractAnchor(loop.cluster1)
overlaps <- findOverlaps(anchor.c1, atac.gr)
anchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c2 <- extractAnchor(loop.cluster2)
overlaps <- findOverlaps(anchor.c2, atac.gr)
anchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c3 <- extractAnchor(loop.cluster3)
overlaps <- findOverlaps(anchor.c3, atac.gr)
anchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c4 <- extractAnchor(loop.cluster4)
overlaps <- findOverlaps(anchor.c4, atac.gr)
anchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c5 <- extractAnchor(loop.cluster5)
overlaps <- findOverlaps(anchor.c5, atac.gr)
anchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c6 <- extractAnchor(loop.cluster6)
overlaps <- findOverlaps(anchor.c6, atac.gr)
anchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c7 <- extractAnchor(loop.cluster7)
overlaps <- findOverlaps(anchor.c7, atac.gr)
anchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c8 <- extractAnchor(loop.cluster8)
overlaps <- findOverlaps(anchor.c8, atac.gr)
anchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
result = runLOLA(anchor.c1, anchor.all, lolaDB)
tb.c1 = as_tibble(result)
result = runLOLA(anchor.c2, anchor.all, lolaDB)
tb.c2 = as_tibble(result)
result = runLOLA(anchor.c3, anchor.all, lolaDB)
tb.c3 = as_tibble(result)
result = runLOLA(anchor.c4, anchor.all, lolaDB)
tb.c4 = as_tibble(result)
result = runLOLA(anchor.c5, anchor.all, lolaDB)
tb.c5 = as_tibble(result)
result = runLOLA(anchor.c6, anchor.all, lolaDB)
tb.c6 = as_tibble(result)
result = runLOLA(anchor.c7, anchor.all, lolaDB)
tb.c7 = as_tibble(result)
result = runLOLA(anchor.c8, anchor.all, lolaDB)
tb.c8 = as_tibble(result)
### HEATMAP
alpha <- 0.05
tb.c1 <- tb.c1 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c2 <- tb.c2 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c3 <- tb.c3 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c4 <- tb.c4 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c5 <- tb.c5 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c6 <- tb.c6 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c7 <- tb.c7 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c8 <- tb.c8 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)
colnames(temp.c1) <- c(\target\, \OR_c1\)
temp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)
colnames(temp.c2) <- c(\target\, \OR_c2\)
temp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)
colnames(temp.c3) <- c(\target\, \OR_c3\)
temp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)
colnames(temp.c4) <- c(\target\, \OR_c4\)
temp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)
colnames(temp.c5) <- c(\target\, \OR_c5\)
temp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)
colnames(temp.c6) <- c(\target\, \OR_c6\)
temp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)
colnames(temp.c7) <- c(\target\, \OR_c7\)
temp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)
colnames(temp.c8) <- c(\target\, \OR_c8\)
temp <- full_join(temp.c1, temp.c2, by = \target\) %>%
full_join(temp.c3, by = \target\) %>%
full_join(temp.c4, by = \target\) %>%
full_join(temp.c5, by = \target\) %>%
full_join(temp.c6, by = \target\) %>%
full_join(temp.c7, by = \target\) %>%
full_join(temp.c8, by = \target\) %>%
mutate_all(~replace_na(., 1))
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
# Initialize lists to store results
anchors <- list()
tbs <- list()
temps <- list()
# Process clusters c1 to c8
for (i in 1:4) {
# Extract anchor
loop_cluster <- get(paste0(\loop.cluster\, i))
anchor <- extractAnchor(loop_cluster)
# Find overlaps and intersect
overlaps <- findOverlaps(anchor, atac.gr)
anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
# Store anchor
anchors[[i]] <- anchor
# Run LOLA
result <- runLOLA(anchor, anchor.all, lolaDB)
tb <- as_tibble(result)
# Filter and summarize
tb <- tb %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
# Store tb
tbs[[i]] <- tb
# Select and rename oddsRatio
temp <- tb %>% dplyr::select(target, oddsRatio)
colnames(temp) <- c(\target\, paste0(\OR_c\, i))
# Store temp
temps[[i]] <- temp
}
# Merge all temp tables into one
temp <- Reduce(function(x, y) full_join(x, y, by = \target\), temps) %>%
mutate_all(~replace_na(., 1))
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\\target\\, \\OR_c1\\)\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\\target\\, \\OR_c2\\)\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\\target\\, \\OR_c3\\)\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\\target\\, \\OR_c4\\)\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\\target\\, \\OR_c5\\)\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\\target\\, \\OR_c6\\)\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\\target\\, \\OR_c7\\)\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\\target\\, \\OR_c8\\)\n\ntemp <- full_join(temp.c1, temp.c2, by = \\target\\) %>%\n  full_join(temp.c3, by = \\target\\) %>%\n  full_join(temp.c4, by = \\target\\) %>%\n  full_join(temp.c5, by = \\target\\) %>%\n  full_join(temp.c6, by = \\target\\) %>%\n  full_join(temp.c7, by = \\target\\) %>%\n  full_join(temp.c8, by = \\target\\) %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\\loop.cluster\\, i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\\target\\, \\OR_c1\\)\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\\target\\, \\OR_c2\\)\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\\target\\, \\OR_c3\\)\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\\target\\, \\OR_c4\\)\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\\target\\, \\OR_c5\\)\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\\target\\, \\OR_c6\\)\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\\target\\, \\OR_c7\\)\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\\target\\, \\OR_c8\\)\n\ntemp <- full_join(temp.c1, temp.c2, by = \\target\\) %>%\n  full_join(temp.c3, by = \\target\\) %>%\n  full_join(temp.c4, by = \\target\\) %>%\n  full_join(temp.c5, by = \\target\\) %>%\n  full_join(temp.c6, by = \\target\\) %>%\n  full_join(temp.c7, by = \\target\\) %>%\n  full_join(temp.c8, by = \\target\\) %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\\loop.cluster\\, i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n```"} -->
```r
```r
### 241015 Testing on subset of loops
anchor.c1 <- extractAnchor(loop.cluster1)
overlaps <- findOverlaps(anchor.c1, atac.gr)
anchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c2 <- extractAnchor(loop.cluster2)
overlaps <- findOverlaps(anchor.c2, atac.gr)
anchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c3 <- extractAnchor(loop.cluster3)
overlaps <- findOverlaps(anchor.c3, atac.gr)
anchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c4 <- extractAnchor(loop.cluster4)
overlaps <- findOverlaps(anchor.c4, atac.gr)
anchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c5 <- extractAnchor(loop.cluster5)
overlaps <- findOverlaps(anchor.c5, atac.gr)
anchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c6 <- extractAnchor(loop.cluster6)
overlaps <- findOverlaps(anchor.c6, atac.gr)
anchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c7 <- extractAnchor(loop.cluster7)
overlaps <- findOverlaps(anchor.c7, atac.gr)
anchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c8 <- extractAnchor(loop.cluster8)
overlaps <- findOverlaps(anchor.c8, atac.gr)
anchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
result = runLOLA(anchor.c1, anchor.all, lolaDB)
tb.c1 = as_tibble(result)
result = runLOLA(anchor.c2, anchor.all, lolaDB)
tb.c2 = as_tibble(result)
result = runLOLA(anchor.c3, anchor.all, lolaDB)
tb.c3 = as_tibble(result)
result = runLOLA(anchor.c4, anchor.all, lolaDB)
tb.c4 = as_tibble(result)
result = runLOLA(anchor.c5, anchor.all, lolaDB)
tb.c5 = as_tibble(result)
result = runLOLA(anchor.c6, anchor.all, lolaDB)
tb.c6 = as_tibble(result)
result = runLOLA(anchor.c7, anchor.all, lolaDB)
tb.c7 = as_tibble(result)
result = runLOLA(anchor.c8, anchor.all, lolaDB)
tb.c8 = as_tibble(result)
### HEATMAP
alpha <- 0.05
tb.c1 <- tb.c1 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c2 <- tb.c2 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c3 <- tb.c3 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c4 <- tb.c4 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c5 <- tb.c5 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c6 <- tb.c6 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c7 <- tb.c7 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c8 <- tb.c8 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)
colnames(temp.c1) <- c(\target\, \OR_c1\)
temp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)
colnames(temp.c2) <- c(\target\, \OR_c2\)
temp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)
colnames(temp.c3) <- c(\target\, \OR_c3\)
temp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)
colnames(temp.c4) <- c(\target\, \OR_c4\)
temp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)
colnames(temp.c5) <- c(\target\, \OR_c5\)
temp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)
colnames(temp.c6) <- c(\target\, \OR_c6\)
temp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)
colnames(temp.c7) <- c(\target\, \OR_c7\)
temp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)
colnames(temp.c8) <- c(\target\, \OR_c8\)
temp <- full_join(temp.c1, temp.c2, by = \target\) %>%
full_join(temp.c3, by = \target\) %>%
full_join(temp.c4, by = \target\) %>%
full_join(temp.c5, by = \target\) %>%
full_join(temp.c6, by = \target\) %>%
full_join(temp.c7, by = \target\) %>%
full_join(temp.c8, by = \target\) %>%
mutate_all(~replace_na(., 1))
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
# Initialize lists to store results
anchors <- list()
tbs <- list()
temps <- list()
# Process clusters c1 to c8
for (i in 1:4) {
# Extract anchor
loop_cluster <- get(paste0(\loop.cluster\, i))
anchor <- extractAnchor(loop_cluster)
# Find overlaps and intersect
overlaps <- findOverlaps(anchor, atac.gr)
anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
# Store anchor
anchors[[i]] <- anchor
# Run LOLA
result <- runLOLA(anchor, anchor.all, lolaDB)
tb <- as_tibble(result)
# Filter and summarize
tb <- tb %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
# Store tb
tbs[[i]] <- tb
# Select and rename oddsRatio
temp <- tb %>% dplyr::select(target, oddsRatio)
colnames(temp) <- c(\target\, paste0(\OR_c\, i))
# Store temp
temps[[i]] <- temp
}
# Merge all temp tables into one
temp <- Reduce(function(x, y) full_join(x, y, by = \target\), temps) %>%
mutate_all(~replace_na(., 1))
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
```
```
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<!-- rnb-output-begin 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-->
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.upno <- bind_rows(loop.up, loop.no)
anchor.upno <- (extractAnchor(loop.upno))
overlaps <- findOverlaps(anchor.upno, atac.gr)
anchor.upno <- pintersect(anchor.upno[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loopNum <- nrow(loop.upno)
# Selecting same number of extreme down loops
loop.down <- loop.all %>% dplyr::filter(diff_dTAG_DMSO < -0.2) %>% dplyr::arrange(diff_dTAG_DMSO) %>% slice_head(n = loopNum)
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
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<!-- rnb-output-begin 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```r
```r
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.upno <- bind_rows(loop.up, loop.no)
anchor.upno <- (extractAnchor(loop.upno))
overlaps <- findOverlaps(anchor.upno, atac.gr)
anchor.upno <- pintersect(anchor.upno[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loopNum <- nrow(loop.upno)
# Selecting same number of extreme down loops
loop.down <- loop.all %>% dplyr::filter(diff_dTAG_DMSO < -0.2) %>% dplyr::arrange(diff_dTAG_DMSO) %>% slice_head(n = loopNum)
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
```
```
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-->\n\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# UPNO\nresult = runLOLA(anchor.upno, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\n# p <- Heatmap(\n#   data,\n#   name = \\Odds Ratio\\,                   # Name of the heatmap legend\n#   cluster_columns = FALSE,            # Remove column dendrogram\n#   row_km = 10,                         # Define the number of k-means clusters for rows (adjust as needed)\n#   show_row_dend = FALSE,\n#   col = col_fun,\n#   border = TRUE\n# )\n# \n# fileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_allAnchorBackground_atac\\)\n# height <- 7\n# width <- 3.5\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\n# tb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\UP\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\NO\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\ntb.upno <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\UP/NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.upno %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac_pub.tsv\\), sep = \\\\t\\)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.down %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac_pub.tsv\\), sep = \\\\t\\)\n\n# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.upno, temp.down)\n# \n# order <- c((temp %>% dplyr::filter(group == \\UP/NO\\) %>% arrange(desc(oddsRatio)))$target, \n#            (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\n# temp$target <- factor(temp$target, levels = rev(order))\ntemp$group <- factor(temp$group, levels = c(\\UP/NO\\, \\DOWN\\))\n\n\ntargetList <- c(\\POLR2A\\, \\CTR9\\,\n                \\AFF4\\, \\ELL2\\,\n                \\MED1\\, \\MED12\\,\n                \\TBP\\, \\TAF1\\, \\TAF3\\,\n                \\ESRRB\\, \\KLF4\\, \\NANOG\\, \\POU5F1\\, \\SOX2\\, \\STAT3\\, \\E2F1\\, \\YY1\\,\n                \\EP300\\, \\DPY30\\, \\EZH2\\, \\KDM2B\\, \\KDB4B\\, \\KDM4C\\, \\KDM6B\\, \\RBBP5\\,\n                \\RAD21\\, \\SMC1A\\, \\SMC3\\, \\CTCF\\,\n                \\JARID2\\, \\SUZ12\\\n)\ntemp <- temp %>% dplyr::filter(target %in% targetList)\n\ntemp$target <- factor(temp$target, levels = rev(targetList))\n# MAX qValueLog to 50\nqValueLogMax <- 50\ntemp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))\np <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 0.5*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_allAnchorBackground_atac_ordered\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(2.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
#fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
#fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\), sep = \\t\)
# UPNO
result = runLOLA(anchor.upno, anchor.all, lolaDB)
tb = as_tibble(result)
#fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
#fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
# p <- Heatmap(
# data,
# name = \Odds Ratio\, # Name of the heatmap legend
# cluster_columns = FALSE, # Remove column dendrogram
# row_km = 10, # Define the number of k-means clusters for rows (adjust as needed)
# show_row_dend = FALSE,
# col = col_fun,
# border = TRUE
# )
#
# fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_allAnchorBackground_atac\)
# height <- 7
# width <- 3.5
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
### Visualizing p-value and OR
alpha <- 0.05
# tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \UP\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
# tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \NO\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
tb.upno <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = \UP/NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.upno %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac_pub.tsv\), sep = \\t\)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.down %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac_pub.tsv\), sep = \\t\)
# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.upno, temp.down)
#
# order <- c((temp %>% dplyr::filter(group == \UP/NO\) %>% arrange(desc(oddsRatio)))$target,
# (temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
# temp$target <- factor(temp$target, levels = rev(order))
temp$group <- factor(temp$group, levels = c(\UP/NO\, \DOWN\))
targetList <- c(\POLR2A\, \CTR9\,
\AFF4\, \ELL2\,
\MED1\, \MED12\,
\TBP\, \TAF1\, \TAF3\,
\ESRRB\, \KLF4\, \NANOG\, \POU5F1\, \SOX2\, \STAT3\, \E2F1\, \YY1\,
\EP300\, \DPY30\, \EZH2\, \KDM2B\, \KDB4B\, \KDM4C\, \KDM6B\, \RBBP5\,
\RAD21\, \SMC1A\, \SMC3\, \CTCF\,
\JARID2\, \SUZ12\
)
temp <- temp %>% dplyr::filter(target %in% targetList)
temp$target <- factor(temp$target, levels = rev(targetList))
# MAX qValueLog to 50
qValueLogMax <- 50
temp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))
p <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 0.5*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_allAnchorBackground_atac_ordered\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(2.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# UPNO\nresult = runLOLA(anchor.upno, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\n# p <- Heatmap(\n#   data,\n#   name = \\Odds Ratio\\,                   # Name of the heatmap legend\n#   cluster_columns = FALSE,            # Remove column dendrogram\n#   row_km = 10,                         # Define the number of k-means clusters for rows (adjust as needed)\n#   show_row_dend = FALSE,\n#   col = col_fun,\n#   border = TRUE\n# )\n# \n# fileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_allAnchorBackground_atac\\)\n# height <- 7\n# width <- 3.5\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\n# tb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\UP\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\NO\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\ntb.upno <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\UP/NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.upno %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac_pub.tsv\\), sep = \\\\t\\)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.down %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac_pub.tsv\\), sep = \\\\t\\)\n\n# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.upno, temp.down)\n# \n# order <- c((temp %>% dplyr::filter(group == \\UP/NO\\) %>% arrange(desc(oddsRatio)))$target, \n#            (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\n# temp$target <- factor(temp$target, levels = rev(order))\ntemp$group <- factor(temp$group, levels = c(\\UP/NO\\, \\DOWN\\))\n\n\ntargetList <- c(\\POLR2A\\, \\CTR9\\,\n                \\AFF4\\, \\ELL2\\,\n                \\MED1\\, \\MED12\\,\n                \\TBP\\, \\TAF1\\, \\TAF3\\,\n                \\ESRRB\\, \\KLF4\\, \\NANOG\\, \\POU5F1\\, \\SOX2\\, \\STAT3\\, \\E2F1\\, \\YY1\\,\n                \\EP300\\, \\DPY30\\, \\EZH2\\, \\KDM2B\\, \\KDB4B\\, \\KDM4C\\, \\KDM6B\\, \\RBBP5\\,\n                \\RAD21\\, \\SMC1A\\, \\SMC3\\, \\CTCF\\,\n                \\JARID2\\, \\SUZ12\\\n)\ntemp <- temp %>% dplyr::filter(target %in% targetList)\n\ntemp$target <- factor(temp$target, levels = rev(targetList))\n# MAX qValueLog to 50\nqValueLogMax <- 50\ntemp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))\np <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 0.5*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_allAnchorBackground_atac_ordered\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(2.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# UPNO\nresult = runLOLA(anchor.upno, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\n#fwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\n# p <- Heatmap(\n#   data,\n#   name = \\Odds Ratio\\,                   # Name of the heatmap legend\n#   cluster_columns = FALSE,            # Remove column dendrogram\n#   row_km = 10,                         # Define the number of k-means clusters for rows (adjust as needed)\n#   show_row_dend = FALSE,\n#   col = col_fun,\n#   border = TRUE\n# )\n# \n# fileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_allAnchorBackground_atac\\)\n# height <- 7\n# width <- 3.5\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\n# tb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\UP\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\NO\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\ntb.upno <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\UP/NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.upno %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac_pub.tsv\\), sep = \\\\t\\)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log10(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\nfwrite(tb.down %>% dplyr::select(c(24, 16, 20,\n                                   4, 25, 5,\n                                   7, 8, 9, 10, 11,\n                                   12, 13, 14)), here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac_pub.tsv\\), sep = \\\\t\\)\n\n# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.upno, temp.down)\n# \n# order <- c((temp %>% dplyr::filter(group == \\UP/NO\\) %>% arrange(desc(oddsRatio)))$target, \n#            (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\n# temp$target <- factor(temp$target, levels = rev(order))\ntemp$group <- factor(temp$group, levels = c(\\UP/NO\\, \\DOWN\\))\n\n\ntargetList <- c(\\POLR2A\\, \\CTR9\\,\n                \\AFF4\\, \\ELL2\\,\n                \\MED1\\, \\MED12\\,\n                \\TBP\\, \\TAF1\\, \\TAF3\\,\n                \\ESRRB\\, \\KLF4\\, \\NANOG\\, \\POU5F1\\, \\SOX2\\, \\STAT3\\, \\E2F1\\, \\YY1\\,\n                \\EP300\\, \\DPY30\\, \\EZH2\\, \\KDM2B\\, \\KDB4B\\, \\KDM4C\\, \\KDM6B\\, \\RBBP5\\,\n                \\RAD21\\, \\SMC1A\\, \\SMC3\\, \\CTCF\\,\n                \\JARID2\\, \\SUZ12\\\n)\ntemp <- temp %>% dplyr::filter(target %in% targetList)\n\ntemp$target <- factor(temp$target, levels = rev(targetList))\n# MAX qValueLog to 50\nqValueLogMax <- 50\ntemp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))\np <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 0.5*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(0.5, 2)) +  # Set min and max point sizes here\n  scale_fill_gradient(low = \\white\\, high = \\#CB333A\\,\n                      limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + \n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_allAnchorBackground_atac_ordered\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(2.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n```\n```"} -->
```r
```r
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
#fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
#fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\), sep = \\t\)
# UPNO
result = runLOLA(anchor.upno, anchor.all, lolaDB)
tb = as_tibble(result)
#fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
#fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
# p <- Heatmap(
# data,
# name = \Odds Ratio\, # Name of the heatmap legend
# cluster_columns = FALSE, # Remove column dendrogram
# row_km = 10, # Define the number of k-means clusters for rows (adjust as needed)
# show_row_dend = FALSE,
# col = col_fun,
# border = TRUE
# )
#
# fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_allAnchorBackground_atac\)
# height <- 7
# width <- 3.5
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
### Visualizing p-value and OR
alpha <- 0.05
# tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \UP\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
# tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \NO\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
tb.upno <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = \UP/NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.upno %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_upno_allLoops_extreme_atac_pub.tsv\), sep = \\t\)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log10(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
fwrite(tb.down %>% dplyr::select(c(24, 16, 20,
4, 25, 5,
7, 8, 9, 10, 11,
12, 13, 14)), here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_allLoops_extreme_atac_pub.tsv\), sep = \\t\)
# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.upno <- tb.upno %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.upno, temp.down)
#
# order <- c((temp %>% dplyr::filter(group == \UP/NO\) %>% arrange(desc(oddsRatio)))$target,
# (temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
# temp$target <- factor(temp$target, levels = rev(order))
temp$group <- factor(temp$group, levels = c(\UP/NO\, \DOWN\))
targetList <- c(\POLR2A\, \CTR9\,
\AFF4\, \ELL2\,
\MED1\, \MED12\,
\TBP\, \TAF1\, \TAF3\,
\ESRRB\, \KLF4\, \NANOG\, \POU5F1\, \SOX2\, \STAT3\, \E2F1\, \YY1\,
\EP300\, \DPY30\, \EZH2\, \KDM2B\, \KDB4B\, \KDM4C\, \KDM6B\, \RBBP5\,
\RAD21\, \SMC1A\, \SMC3\, \CTCF\,
\JARID2\, \SUZ12\
)
temp <- temp %>% dplyr::filter(target %in% targetList)
temp$target <- factor(temp$target, levels = rev(targetList))
# MAX qValueLog to 50
qValueLogMax <- 50
temp2 <- temp %>% dplyr::mutate(qValueLog = min(qValueLog, qValueLogMax))
p <- ggplot(temp2, aes(x = group, y = target, fill = oddsRatio, size = qValueLog)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 0.5*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(0.5, 2)) + # Set min and max point sizes here
scale_fill_gradient(low = \white\, high = \#CB333A\,
limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_allAnchorBackground_atac_ordered\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(2.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Limited to ATAC-seq signal, all loop backgroup
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_all_dTAGvsDMSO_UP_diff0.5.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_all_dTAGvsDMSO_NO_diff0.5.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_all_dTAGvsDMSO_DOWN_diff0.5.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
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```r
```r
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_all_dTAGvsDMSO_UP_diff0.5.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_all_dTAGvsDMSO_NO_diff0.5.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_all_dTAGvsDMSO_DOWN_diff0.5.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
```
```
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-->\n\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 4,                        # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_allLoops_allAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n############\n# \n# ### Visualizing p-value and OR\n# alpha <- 0.05\n# tb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\UP\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\NO\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\DOWN\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# \n# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# \n# temp <- bind_rows(temp.up, temp.down)\n# \n# order <- c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n#            (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\n# temp$target <- factor(temp$target, levels = rev(order))\n# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n#   geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n#   labs(x = NULL, y = NULL)  +   scale_size_continuous(range = c(1, 3)) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_allLoops_OR_allAnchorBackground_atac\\)\n# height <-3\n# width <- 2\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 4, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_allLoops_allAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
############
#
# ### Visualizing p-value and OR
# alpha <- 0.05
# tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \UP\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
# tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \NO\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
# tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \DOWN\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
#
# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
# temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
#
# temp <- bind_rows(temp.up, temp.down)
#
# order <- c((temp %>% dplyr::filter(group == \UP\) %>% arrange(desc(oddsRatio)))$target,
# (temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
# temp$target <- factor(temp$target, levels = rev(order))
# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
# geom_point() + theme_bw() + scale_color_gradient(low = \blue\, high = \red\) +
# labs(x = NULL, y = NULL) + scale_size_continuous(range = c(1, 3)) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_allLoops_OR_allAnchorBackground_atac\)
# height <-3
# width <- 2
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 4,                        # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_allLoops_allAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n############\n# \n# ### Visualizing p-value and OR\n# alpha <- 0.05\n# tb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\UP\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\NO\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# tb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\\)) %>%\n#   dplyr::mutate(target = toupper(antibody),\n#                 qValueLog = -log2(qValue),\n#                 group = \\DOWN\\) %>%\n#   filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n#   dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n#   slice_min(meanRnk, with_ties = FALSE)\n# \n# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n# \n# temp <- bind_rows(temp.up, temp.down)\n# \n# order <- c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n#            (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\n# temp$target <- factor(temp$target, levels = rev(order))\n# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n#   geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n#   labs(x = NULL, y = NULL)  +   scale_size_continuous(range = c(1, 3)) +\n#   theme(axis.text = element_text(size = 6),  # Set axis text size\n#         axis.title = element_text(size = 6), # Set axis title size (if not removed)\n#         legend.text = element_text(size = 6), # Set legend text size\n#         legend.title = element_text(size = 6)) \n# \n# fileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_allLoops_OR_allAnchorBackground_atac\\)\n# height <-3\n# width <- 2\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\n# svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n# print(p)\n# dev.off()\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 4, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_allLoops_allAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
############
#
# ### Visualizing p-value and OR
# alpha <- 0.05
# tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_allLoops_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \UP\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
# tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_allLoops_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \NO\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
# tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_allLoops_atac.tsv\)) %>%
# dplyr::mutate(target = toupper(antibody),
# qValueLog = -log2(qValue),
# group = \DOWN\) %>%
# filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
# dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
# slice_min(meanRnk, with_ties = FALSE)
#
# temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
# temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
# temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
#
# temp <- bind_rows(temp.up, temp.down)
#
# order <- c((temp %>% dplyr::filter(group == \UP\) %>% arrange(desc(oddsRatio)))$target,
# (temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
# temp$target <- factor(temp$target, levels = rev(order))
# p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
# geom_point() + theme_bw() + scale_color_gradient(low = \blue\, high = \red\) +
# labs(x = NULL, y = NULL) + scale_size_continuous(range = c(1, 3)) +
# theme(axis.text = element_text(size = 6), # Set axis text size
# axis.title = element_text(size = 6), # Set axis title size (if not removed)
# legend.text = element_text(size = 6), # Set legend text size
# legend.title = element_text(size = 6))
#
# fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_allLoops_OR_allAnchorBackground_atac\)
# height <-3
# width <- 2
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
# svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
# print(p)
# dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### dTAG, obsexp
##### - Limited to ATAC-seq signal, all loop backgroup
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_pe-pe_dTAGvsDMSO_UP_diff0.5.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_pe-pe_dTAGvsDMSO_NO_diff0.5.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_pe-pe_dTAGvsDMSO_DOWN_diff0.5.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_pe-pe_dTAGvsDMSO_UP_diff0.5.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_pe-pe_dTAGvsDMSO_NO_diff0.5.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_logOE_pe-pe_dTAGvsDMSO_DOWN_diff0.5.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
```
```
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") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \"LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\")) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \"DOWN\") %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \"UP\") %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \"DOWN\") %>% arrange(desc(oddsRatio)))$target)\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \"blue\", high = \"red\") +\n  labs(x = NULL, y = NULL)  +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\"anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\")\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\"target\", \"OR_c1\")\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\"target\", \"OR_c2\")\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\"target\", \"OR_c3\")\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\"target\", \"OR_c4\")\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\"target\", \"OR_c5\")\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\"target\", \"OR_c6\")\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\"target\", \"OR_c7\")\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\"target\", \"OR_c8\")\n\ntemp <- full_join(temp.c1, temp.c2, by = \"target\") %>%\n  full_join(temp.c3, by = \"target\") %>%\n  full_join(temp.c4, by = \"target\") %>%\n  full_join(temp.c5, by = \"target\") %>%\n  full_join(temp.c6, by = \"target\") %>%\n  full_join(temp.c7, by = \"target\") %>%\n  full_join(temp.c8, by = \"target\") %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\"target\") %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\"white\", \"red\"))\n\n\n#fviz_nbclust(data, kmeans, method = \"wss\")\n\np <- Heatmap(\n  data,\n  name = \"Odds Ratio\",                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\"loop.cluster\", i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\"target\", paste0(\"OR_c\", i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \"target\"), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\"target\") %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\"white\", \"red\"))\n\n\n#fviz_nbclust(data, kmeans, method = \"wss\")\n\np <- Heatmap(\n  data,\n  name = \"Odds Ratio\",                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```"} -->\n\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun                       # Use the red gradient color scale\n)\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_pe-peLoops_regAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\UP\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n  labs(x = NULL, y = NULL)  +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\\)\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\\target\\, \\OR_c1\\)\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\\target\\, \\OR_c2\\)\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\\target\\, \\OR_c3\\)\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\\target\\, \\OR_c4\\)\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\\target\\, \\OR_c5\\)\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\\target\\, \\OR_c6\\)\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\\target\\, \\OR_c7\\)\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\\target\\, \\OR_c8\\)\n\ntemp <- full_join(temp.c1, temp.c2, by = \\target\\) %>%\n  full_join(temp.c3, by = \\target\\) %>%\n  full_join(temp.c4, by = \\target\\) %>%\n  full_join(temp.c5, by = \\target\\) %>%\n  full_join(temp.c6, by = \\target\\) %>%\n  full_join(temp.c7, by = \\target\\) %>%\n  full_join(temp.c8, by = \\target\\) %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\\loop.cluster\\, i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n\n<!-- rnb-source-end -->\n"} -->
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 4, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun # Use the red gradient color scale
)
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_pe-peLoops_regAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Visualizing p-value and OR
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \UP\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.up, temp.down)
order <- c((temp %>% dplyr::filter(group == \UP\) %>% arrange(desc(oddsRatio)))$target,
(temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
temp$target <- factor(temp$target, levels = rev(order))
p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
geom_point() + theme_bw() + scale_color_gradient(low = \blue\, high = \red\) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\)
height <-3
width <- 2
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### 241015 Testing on subset of loops
anchor.c1 <- extractAnchor(loop.cluster1)
overlaps <- findOverlaps(anchor.c1, atac.gr)
anchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c2 <- extractAnchor(loop.cluster2)
overlaps <- findOverlaps(anchor.c2, atac.gr)
anchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c3 <- extractAnchor(loop.cluster3)
overlaps <- findOverlaps(anchor.c3, atac.gr)
anchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c4 <- extractAnchor(loop.cluster4)
overlaps <- findOverlaps(anchor.c4, atac.gr)
anchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c5 <- extractAnchor(loop.cluster5)
overlaps <- findOverlaps(anchor.c5, atac.gr)
anchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c6 <- extractAnchor(loop.cluster6)
overlaps <- findOverlaps(anchor.c6, atac.gr)
anchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c7 <- extractAnchor(loop.cluster7)
overlaps <- findOverlaps(anchor.c7, atac.gr)
anchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c8 <- extractAnchor(loop.cluster8)
overlaps <- findOverlaps(anchor.c8, atac.gr)
anchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
result = runLOLA(anchor.c1, anchor.all, lolaDB)
tb.c1 = as_tibble(result)
result = runLOLA(anchor.c2, anchor.all, lolaDB)
tb.c2 = as_tibble(result)
result = runLOLA(anchor.c3, anchor.all, lolaDB)
tb.c3 = as_tibble(result)
result = runLOLA(anchor.c4, anchor.all, lolaDB)
tb.c4 = as_tibble(result)
result = runLOLA(anchor.c5, anchor.all, lolaDB)
tb.c5 = as_tibble(result)
result = runLOLA(anchor.c6, anchor.all, lolaDB)
tb.c6 = as_tibble(result)
result = runLOLA(anchor.c7, anchor.all, lolaDB)
tb.c7 = as_tibble(result)
result = runLOLA(anchor.c8, anchor.all, lolaDB)
tb.c8 = as_tibble(result)
### HEATMAP
alpha <- 0.05
tb.c1 <- tb.c1 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c2 <- tb.c2 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c3 <- tb.c3 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c4 <- tb.c4 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c5 <- tb.c5 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c6 <- tb.c6 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c7 <- tb.c7 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c8 <- tb.c8 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)
colnames(temp.c1) <- c(\target\, \OR_c1\)
temp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)
colnames(temp.c2) <- c(\target\, \OR_c2\)
temp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)
colnames(temp.c3) <- c(\target\, \OR_c3\)
temp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)
colnames(temp.c4) <- c(\target\, \OR_c4\)
temp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)
colnames(temp.c5) <- c(\target\, \OR_c5\)
temp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)
colnames(temp.c6) <- c(\target\, \OR_c6\)
temp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)
colnames(temp.c7) <- c(\target\, \OR_c7\)
temp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)
colnames(temp.c8) <- c(\target\, \OR_c8\)
temp <- full_join(temp.c1, temp.c2, by = \target\) %>%
full_join(temp.c3, by = \target\) %>%
full_join(temp.c4, by = \target\) %>%
full_join(temp.c5, by = \target\) %>%
full_join(temp.c6, by = \target\) %>%
full_join(temp.c7, by = \target\) %>%
full_join(temp.c8, by = \target\) %>%
mutate_all(~replace_na(., 1))
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
# Initialize lists to store results
anchors <- list()
tbs <- list()
temps <- list()
# Process clusters c1 to c8
for (i in 1:4) {
# Extract anchor
loop_cluster <- get(paste0(\loop.cluster\, i))
anchor <- extractAnchor(loop_cluster)
# Find overlaps and intersect
overlaps <- findOverlaps(anchor, atac.gr)
anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
# Store anchor
anchors[[i]] <- anchor
# Run LOLA
result <- runLOLA(anchor, anchor.all, lolaDB)
tb <- as_tibble(result)
# Filter and summarize
tb <- tb %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
# Store tb
tbs[[i]] <- tb
# Select and rename oddsRatio
temp <- tb %>% dplyr::select(target, oddsRatio)
colnames(temp) <- c(\target\, paste0(\OR_c\, i))
# Store temp
temps[[i]] <- temp
}
# Merge all temp tables into one
temp <- Reduce(function(x, y) full_join(x, y, by = \target\), temps) %>%
mutate_all(~replace_na(., 1))
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin {"data":"```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun                       # Use the red gradient color scale\n)\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_pe-peLoops_regAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\UP\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n  labs(x = NULL, y = NULL)  +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\\)\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\\target\\, \\OR_c1\\)\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\\target\\, \\OR_c2\\)\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\\target\\, \\OR_c3\\)\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\\target\\, \\OR_c4\\)\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\\target\\, \\OR_c5\\)\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\\target\\, \\OR_c6\\)\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\\target\\, \\OR_c7\\)\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\\target\\, \\OR_c8\\)\n\ntemp <- full_join(temp.c1, temp.c2, by = \\target\\) %>%\n  full_join(temp.c3, by = \\target\\) %>%\n  full_join(temp.c4, by = \\target\\) %>%\n  full_join(temp.c5, by = \\target\\) %>%\n  full_join(temp.c6, by = \\target\\) %>%\n  full_join(temp.c7, by = \\target\\) %>%\n  full_join(temp.c8, by = \\target\\) %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\\loop.cluster\\, i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n```"} -->\n\n```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun                       # Use the red gradient color scale\n)\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_pe-peLoops_regAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\UP\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n  labs(x = NULL, y = NULL)  +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\\)\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\\target\\, \\OR_c1\\)\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\\target\\, \\OR_c2\\)\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\\target\\, \\OR_c3\\)\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\\target\\, \\OR_c4\\)\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\\target\\, \\OR_c5\\)\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\\target\\, \\OR_c6\\)\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\\target\\, \\OR_c7\\)\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\\target\\, \\OR_c8\\)\n\ntemp <- full_join(temp.c1, temp.c2, by = \\target\\) %>%\n  full_join(temp.c3, by = \\target\\) %>%\n  full_join(temp.c4, by = \\target\\) %>%\n  full_join(temp.c5, by = \\target\\) %>%\n  full_join(temp.c6, by = \\target\\) %>%\n  full_join(temp.c7, by = \\target\\) %>%\n  full_join(temp.c8, by = \\target\\) %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\\loop.cluster\\, i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\\), sep = \\\\t\\)\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun                       # Use the red gradient color scale\n)\n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_pe-peLoops_regAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\UP\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n  labs(x = NULL, y = NULL)  +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\\)\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n\n### 241015 Testing on subset of loops\nanchor.c1 <- extractAnchor(loop.cluster1)\noverlaps <- findOverlaps(anchor.c1, atac.gr)\nanchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c2 <- extractAnchor(loop.cluster2)\noverlaps <- findOverlaps(anchor.c2, atac.gr)\nanchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c3 <- extractAnchor(loop.cluster3)\noverlaps <- findOverlaps(anchor.c3, atac.gr)\nanchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c4 <- extractAnchor(loop.cluster4)\noverlaps <- findOverlaps(anchor.c4, atac.gr)\nanchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c5 <- extractAnchor(loop.cluster5)\noverlaps <- findOverlaps(anchor.c5, atac.gr)\nanchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c6 <- extractAnchor(loop.cluster6)\noverlaps <- findOverlaps(anchor.c6, atac.gr)\nanchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c7 <- extractAnchor(loop.cluster7)\noverlaps <- findOverlaps(anchor.c7, atac.gr)\nanchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nanchor.c8 <- extractAnchor(loop.cluster8)\noverlaps <- findOverlaps(anchor.c8, atac.gr)\nanchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\nresult = runLOLA(anchor.c1, anchor.all, lolaDB)\ntb.c1 = as_tibble(result)\n\nresult = runLOLA(anchor.c2, anchor.all, lolaDB)\ntb.c2 = as_tibble(result)\n\nresult = runLOLA(anchor.c3, anchor.all, lolaDB)\ntb.c3 = as_tibble(result)\n\nresult = runLOLA(anchor.c4, anchor.all, lolaDB)\ntb.c4 = as_tibble(result)\nresult = runLOLA(anchor.c5, anchor.all, lolaDB)\ntb.c5 = as_tibble(result)\n\nresult = runLOLA(anchor.c6, anchor.all, lolaDB)\ntb.c6 = as_tibble(result)\n\nresult = runLOLA(anchor.c7, anchor.all, lolaDB)\ntb.c7 = as_tibble(result)\n\nresult = runLOLA(anchor.c8, anchor.all, lolaDB)\ntb.c8 = as_tibble(result)\n\n### HEATMAP\nalpha <- 0.05\ntb.c1 <- tb.c1 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c2 <- tb.c2 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c3 <- tb.c3 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c4 <- tb.c4 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.c5 <- tb.c5 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c6 <- tb.c6 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c7 <- tb.c7 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntb.c8 <- tb.c8 %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>%\n  dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c1) <- c(\\target\\, \\OR_c1\\)\ntemp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c2) <- c(\\target\\, \\OR_c2\\)\ntemp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c3) <- c(\\target\\, \\OR_c3\\)\ntemp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c4) <- c(\\target\\, \\OR_c4\\)\ntemp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c5) <- c(\\target\\, \\OR_c5\\)\ntemp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c6) <- c(\\target\\, \\OR_c6\\)\ntemp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c7) <- c(\\target\\, \\OR_c7\\)\ntemp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)\ncolnames(temp.c8) <- c(\\target\\, \\OR_c8\\)\n\ntemp <- full_join(temp.c1, temp.c2, by = \\target\\) %>%\n  full_join(temp.c3, by = \\target\\) %>%\n  full_join(temp.c4, by = \\target\\) %>%\n  full_join(temp.c5, by = \\target\\) %>%\n  full_join(temp.c6, by = \\target\\) %>%\n  full_join(temp.c7, by = \\target\\) %>%\n  full_join(temp.c8, by = \\target\\) %>%\n  mutate_all(~replace_na(., 1))\n\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\n\n# Initialize lists to store results\nanchors <- list()\ntbs <- list()\ntemps <- list()\n\n# Process clusters c1 to c8\nfor (i in 1:4) {\n  # Extract anchor\n  loop_cluster <- get(paste0(\\loop.cluster\\, i))\n  anchor <- extractAnchor(loop_cluster)\n  \n  # Find overlaps and intersect\n  overlaps <- findOverlaps(anchor, atac.gr)\n  anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n  \n  # Store anchor\n  anchors[[i]] <- anchor\n  \n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n```"} -->
```r
```r
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_up_pe-peLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_no_pe-peLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_logOE_diff0.5_down_pe-peLoops_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 4, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun # Use the red gradient color scale
)
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_logOE_diff0.5_pe-peLoops_regAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Visualizing p-value and OR
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \UP\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_dTAG_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.up, temp.down)
order <- c((temp %>% dplyr::filter(group == \UP\) %>% arrange(desc(oddsRatio)))$target,
(temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
temp$target <- factor(temp$target, levels = rev(order))
p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
geom_point() + theme_bw() + scale_color_gradient(low = \blue\, high = \red\) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\)
height <-3
width <- 2
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### 241015 Testing on subset of loops
anchor.c1 <- extractAnchor(loop.cluster1)
overlaps <- findOverlaps(anchor.c1, atac.gr)
anchor.c1 <- pintersect(anchor.c1[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c2 <- extractAnchor(loop.cluster2)
overlaps <- findOverlaps(anchor.c2, atac.gr)
anchor.c2 <- pintersect(anchor.c2[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c3 <- extractAnchor(loop.cluster3)
overlaps <- findOverlaps(anchor.c3, atac.gr)
anchor.c3 <- pintersect(anchor.c3[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c4 <- extractAnchor(loop.cluster4)
overlaps <- findOverlaps(anchor.c4, atac.gr)
anchor.c4 <- pintersect(anchor.c4[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c5 <- extractAnchor(loop.cluster5)
overlaps <- findOverlaps(anchor.c5, atac.gr)
anchor.c5 <- pintersect(anchor.c5[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c6 <- extractAnchor(loop.cluster6)
overlaps <- findOverlaps(anchor.c6, atac.gr)
anchor.c6 <- pintersect(anchor.c6[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c7 <- extractAnchor(loop.cluster7)
overlaps <- findOverlaps(anchor.c7, atac.gr)
anchor.c7 <- pintersect(anchor.c7[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchor.c8 <- extractAnchor(loop.cluster8)
overlaps <- findOverlaps(anchor.c8, atac.gr)
anchor.c8 <- pintersect(anchor.c8[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
result = runLOLA(anchor.c1, anchor.all, lolaDB)
tb.c1 = as_tibble(result)
result = runLOLA(anchor.c2, anchor.all, lolaDB)
tb.c2 = as_tibble(result)
result = runLOLA(anchor.c3, anchor.all, lolaDB)
tb.c3 = as_tibble(result)
result = runLOLA(anchor.c4, anchor.all, lolaDB)
tb.c4 = as_tibble(result)
result = runLOLA(anchor.c5, anchor.all, lolaDB)
tb.c5 = as_tibble(result)
result = runLOLA(anchor.c6, anchor.all, lolaDB)
tb.c6 = as_tibble(result)
result = runLOLA(anchor.c7, anchor.all, lolaDB)
tb.c7 = as_tibble(result)
result = runLOLA(anchor.c8, anchor.all, lolaDB)
tb.c8 = as_tibble(result)
### HEATMAP
alpha <- 0.05
tb.c1 <- tb.c1 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c2 <- tb.c2 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c3 <- tb.c3 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c4 <- tb.c4 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c5 <- tb.c5 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c6 <- tb.c6 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c7 <- tb.c7 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.c8 <- tb.c8 %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.c1 <- tb.c1 %>% dplyr::select(target, oddsRatio)
colnames(temp.c1) <- c(\target\, \OR_c1\)
temp.c2 <- tb.c2 %>% dplyr::select(target, oddsRatio)
colnames(temp.c2) <- c(\target\, \OR_c2\)
temp.c3 <- tb.c3 %>% dplyr::select(target, oddsRatio)
colnames(temp.c3) <- c(\target\, \OR_c3\)
temp.c4 <- tb.c4 %>% dplyr::select(target, oddsRatio)
colnames(temp.c4) <- c(\target\, \OR_c4\)
temp.c5 <- tb.c5 %>% dplyr::select(target, oddsRatio)
colnames(temp.c5) <- c(\target\, \OR_c5\)
temp.c6 <- tb.c6 %>% dplyr::select(target, oddsRatio)
colnames(temp.c6) <- c(\target\, \OR_c6\)
temp.c7 <- tb.c7 %>% dplyr::select(target, oddsRatio)
colnames(temp.c7) <- c(\target\, \OR_c7\)
temp.c8 <- tb.c8 %>% dplyr::select(target, oddsRatio)
colnames(temp.c8) <- c(\target\, \OR_c8\)
temp <- full_join(temp.c1, temp.c2, by = \target\) %>%
full_join(temp.c3, by = \target\) %>%
full_join(temp.c4, by = \target\) %>%
full_join(temp.c5, by = \target\) %>%
full_join(temp.c6, by = \target\) %>%
full_join(temp.c7, by = \target\) %>%
full_join(temp.c8, by = \target\) %>%
mutate_all(~replace_na(., 1))
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
# Initialize lists to store results
anchors <- list()
tbs <- list()
temps <- list()
# Process clusters c1 to c8
for (i in 1:4) {
# Extract anchor
loop_cluster <- get(paste0(\loop.cluster\, i))
anchor <- extractAnchor(loop_cluster)
# Find overlaps and intersect
overlaps <- findOverlaps(anchor, atac.gr)
anchor <- pintersect(anchor[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
# Store anchor
anchors[[i]] <- anchor
# Run LOLA
result <- runLOLA(anchor, anchor.all, lolaDB)
tb <- as_tibble(result)
# Filter and summarize
tb <- tb %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
# Store tb
tbs[[i]] <- tb
# Select and rename oddsRatio
temp <- tb %>% dplyr::select(target, oddsRatio)
colnames(temp) <- c(\target\, paste0(\OR_c\, i))
# Store temp
temps[[i]] <- temp
}
# Merge all temp tables into one
temp <- Reduce(function(x, y) full_join(x, y, by = \target\), temps) %>%
mutate_all(~replace_na(., 1))
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Limiting to ATAC-seq signal, reg loop background
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atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loopNum <- nrow(loop.down)
# Selecting same number of extreme no loops
loop.no <- loop.all %>%
dplyr::mutate(absDiff = abs(diff_A485_DMSO)) %>%
dplyr::filter(absDiff < 0.2) %>%
dplyr::arrange(absDiff) %>% slice_head(n = loopNum)
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
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```r
```r
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loopNum <- nrow(loop.down)
# Selecting same number of extreme no loops
loop.no <- loop.all %>%
dplyr::mutate(absDiff = abs(diff_A485_DMSO)) %>%
dplyr::filter(absDiff < 0.2) %>%
dplyr::arrange(absDiff) %>% slice_head(n = loopNum)
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
```
```
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 5,                         # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\nfileName <- paste0(\\anchorLOLA_A485_vs_DMSO_diff0.2_allLoops_extreme_regAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\UP\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- unique(c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target))\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n  labs(x = NULL, y = NULL)  +   scale_size_continuous(range = c(1, 3)) +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\\anchorLOLA_A485_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_regAnchorBackground_atac\\)\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 5, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
fileName <- paste0(\anchorLOLA_A485_vs_DMSO_diff0.2_allLoops_extreme_regAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Visualizing p-value and OR
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \UP\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.up, temp.down)
order <- unique(c((temp %>% dplyr::filter(group == \UP\) %>% arrange(desc(oddsRatio)))$target,
(temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target))
temp$target <- factor(temp$target, levels = rev(order))
p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
geom_point() + theme_bw() + scale_color_gradient(low = \blue\, high = \red\) +
labs(x = NULL, y = NULL) + scale_size_continuous(range = c(1, 3)) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- paste0(\anchorLOLA_A485_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_regAnchorBackground_atac\)
height <-3
width <- 2
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 5,                         # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n\nfileName <- paste0(\\anchorLOLA_A485_vs_DMSO_diff0.2_allLoops_extreme_regAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\UP\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- unique(c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target))\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n  labs(x = NULL, y = NULL)  +   scale_size_continuous(range = c(1, 3)) +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\\anchorLOLA_A485_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_regAnchorBackground_atac\\)\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 5, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
fileName <- paste0(\anchorLOLA_A485_vs_DMSO_diff0.2_allLoops_extreme_regAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Visualizing p-value and OR
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \UP\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_allLoops_extreme_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.up, temp.down)
order <- unique(c((temp %>% dplyr::filter(group == \UP\) %>% arrange(desc(oddsRatio)))$target,
(temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target))
temp$target <- factor(temp$target, levels = rev(order))
p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
geom_point() + theme_bw() + scale_color_gradient(low = \blue\, high = \red\) +
labs(x = NULL, y = NULL) + scale_size_continuous(range = c(1, 3)) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- paste0(\anchorLOLA_A485_vs_DMSO_diff0.2_allLoops_extreme_OR_qValue_regAnchorBackground_atac\)
height <-3
width <- 2
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### A485
##### - Limited to ATAC-seq signal, all loop backgroupd
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<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVlYUmhZeUE4TFNCbWNtVmhaQ2hvWlhKbEtISmxaa1JwY2l3Z1hGeEhVMDB6TVRBMk1qVTNYMEZVUVVOZlJWTkRYekV1WW1Wa1hGd3BLU0FsUGlVZ1pIQnNlWEk2T25ObGJHVmpkQ2hXTVN3Z1ZqSXNJRll6S1Z4dVkyOXNibUZ0WlhNb1lYUmhZeWtnUEMwZ1l5aGNYR05vY2x4Y0xDQmNYSE4wWVhKMFhGd3NJRnhjWlc1a1hGd3BYRzVoZEdGakxtZHlJRHd0SUcxaGEyVkhVbUZ1WjJWelJuSnZiVVJoZEdGR2NtRnRaU2hoZEdGaktWeHVYRzRqSUV4UFFVUkpUa2NnVEU5UFVGTmNiaU1qSXlCSmJYQnZjblJwYm1jZ1pHbG1abVZ5Wlc1MGFXRnNJSEpsWjNWc1lYUnZjbmtnYkc5dmNITWdKaUJsZUhSeVlXTjBJR0Z1WTJodmNseHViRzl2Y0M1aGJHd2dQQzBnWm5KbFlXUW9hR1Z5WlNoamIyNXpaVzV6ZFhORWFYSXNJRnhjWTJoeWIyMXZYMk52Ym5OZllXNXViMGhwWlhKaGNtTm9lUzUwYzNaY1hDa3BJQ1UrSlNCa2NHeDVjam82Wm1sc2RHVnlLRUZ1Ym04eUlDVnBiaVVnWXloY1hGQXRVRnhjTENCY1hGQXRSVnhjTENCY1hFVXRSVnhjS1NsY2JtRnVZMmh2Y2k1aGJHd2dQQzBnS0dWNGRISmhZM1JCYm1Ob2IzSW9iRzl2Y0M1aGJHd3BLVnh1YjNabGNteGhjSE1nUEMwZ1ptbHVaRTkyWlhKc1lYQnpLR0Z1WTJodmNpNWhiR3dzSUdGMFlXTXVaM0lwWEc1aGJtTm9iM0l1WVd4c0lEd3RJSEJwYm5SbGNuTmxZM1FvWVc1amFHOXlMbUZzYkZ0eGRXVnllVWhwZEhNb2IzWmxjbXhoY0hNcFhTd2dZWFJoWXk1bmNsdHpkV0pxWldOMFNHbDBjeWh2ZG1WeWJHRndjeWxkS1Z4dVhHNXNiMjl3TG5Wd0lEd3RJR1p5WldGa0tHaGxjbVVvWTI5dWMyVnVjM1Z6UkdseUxDQmNYR05vY205dGIxOWpiMjV6WDJGdWJtOUlhV1Z5WVhKamFIbGZjR1V0Y0dWZlFUUTROWFp6UkUxVFQxOVZVRjlrYVdabU1DNHlMbUpsWkhCbFhGd3BLVnh1WVc1amFHOXlMblZ3SUR3dElDaGxlSFJ5WVdOMFFXNWphRzl5S0d4dmIzQXVkWEFwS1Z4dWIzWmxjbXhoY0hNZ1BDMGdabWx1WkU5MlpYSnNZWEJ6S0dGdVkyaHZjaTUxY0N3Z1lYUmhZeTVuY2lsY2JtRnVZMmh2Y2k1MWNDQThMU0J3YVc1MFpYSnpaV04wS0dGdVkyaHZjaTUxY0Z0eGRXVnllVWhwZEhNb2IzWmxjbXhoY0hNcFhTd2dZWFJoWXk1bmNsdHpkV0pxWldOMFNHbDBjeWh2ZG1WeWJHRndjeWxkS1Z4dVhHNXNiMjl3TG01dklEd3RJR1p5WldGa0tHaGxjbVVvWTI5dWMyVnVjM1Z6UkdseUxDQmNYR05vY205dGIxOWpiMjV6WDJGdWJtOUlhV1Z5WVhKamFIbGZjR1V0Y0dWZlFUUTROWFp6UkUxVFQxOU9UMTlrYVdabU1DNHlMbUpsWkhCbFhGd3BLVnh1WVc1amFHOXlMbTV2SUR3dElDaGxlSFJ5WVdOMFFXNWphRzl5S0d4dmIzQXVibThwS1Z4dWIzWmxjbXhoY0hNZ1BDMGdabWx1WkU5MlpYSnNZWEJ6S0dGdVkyaHZjaTV1Ynl3Z1lYUmhZeTVuY2lsY2JtRnVZMmh2Y2k1dWJ5QThMU0J3YVc1MFpYSnpaV04wS0dGdVkyaHZjaTV1YjF0eGRXVnllVWhwZEhNb2IzWmxjbXhoY0hNcFhTd2dZWFJoWXk1bmNsdHpkV0pxWldOMFNHbDBjeWh2ZG1WeWJHRndjeWxkS1Z4dVhHNXNiMjl3TG1SdmQyNGdQQzBnWm5KbFlXUW9hR1Z5WlNoamIyNXpaVzV6ZFhORWFYSXNJRnhjWTJoeWIyMXZYMk52Ym5OZllXNXViMGhwWlhKaGNtTm9lVjl3WlMxd1pWOUJORGcxZG5ORVRWTlBYMFJQVjA1ZlpHbG1aakF1TWk1aVpXUndaVnhjS1NsY2JtRnVZMmh2Y2k1a2IzZHVJRHd0SUNobGVIUnlZV04wUVc1amFHOXlLR3h2YjNBdVpHOTNiaWtwWEc1dmRtVnliR0Z3Y3lBOExTQm1hVzVrVDNabGNteGhjSE1vWVc1amFHOXlMbVJ2ZDI0c0lHRjBZV011WjNJcFhHNWhibU5vYjNJdVpHOTNiaUE4TFNCd2FXNTBaWEp6WldOMEtHRnVZMmh2Y2k1a2IzZHVXM0YxWlhKNVNHbDBjeWh2ZG1WeWJHRndjeWxkTENCaGRHRmpMbWR5VzNOMVltcGxZM1JJYVhSektHOTJaWEpzWVhCektWMHBYRzVjYmx4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcbmF0YWMgPC0gZnJlYWQoaGVyZShyZWZEaXIsIFxcR1NNMzEwNjI1N19BVEFDX0VTQ18xLmJlZFxcKSkgJT4lIGRwbHlyOjpzZWxlY3QoVjEsIFYyLCBWMylcbmNvbG5hbWVzKGF0YWMpIDwtIGMoXFxjaHJcXCwgXFxzdGFydFxcLCBcXGVuZFxcKVxuYXRhYy5nciA8LSBtYWtlR1Jhbmdlc0Zyb21EYXRhRnJhbWUoYXRhYylcblxuIyBMT0FESU5HIExPT1BTXG4jIyMgSW1wb3J0aW5nIGRpZmZlcmVudGlhbCByZWd1bGF0b3J5IGxvb3BzICYgZXh0cmFjdCBhbmNob3Jcbmxvb3AuYWxsIDwtIGZyZWFkKGhlcmUoY29uc2Vuc3VzRGlyLCBcXGNocm9tb19jb25zX2Fubm9IaWVyYXJjaHkudHN2XFwpKSAlPiUgZHBseXI6OmZpbHRlcihBbm5vMiAlaW4lIGMoXFxQLVBcXCwgXFxQLUVcXCwgXFxFLUVcXCkpXG5hbmNob3IuYWxsIDwtIChleHRyYWN0QW5jaG9yKGxvb3AuYWxsKSlcbm92ZXJsYXBzIDwtIGZpbmRPdmVybGFwcyhhbmNob3IuYWxsLCBhdGFjLmdyKVxuYW5jaG9yLmFsbCA8LSBwaW50ZXJzZWN0KGFuY2hvci5hbGxbcXVlcnlIaXRzKG92ZXJsYXBzKV0sIGF0YWMuZ3Jbc3ViamVjdEhpdHMob3ZlcmxhcHMpXSlcblxubG9vcC51cCA8LSBmcmVhZChoZXJlKGNvbnNlbnN1c0RpciwgXFxjaHJvbW9fY29uc19hbm5vSGllcmFyY2h5X3BlLXBlX0E0ODV2c0RNU09fVVBfZGlmZjAuMi5iZWRwZVxcKSlcbmFuY2hvci51cCA8LSAoZXh0cmFjdEFuY2hvcihsb29wLnVwKSlcbm92ZXJsYXBzIDwtIGZpbmRPdmVybGFwcyhhbmNob3IudXAsIGF0YWMuZ3IpXG5hbmNob3IudXAgPC0gcGludGVyc2VjdChhbmNob3IudXBbcXVlcnlIaXRzKG92ZXJsYXBzKV0sIGF0YWMuZ3Jbc3ViamVjdEhpdHMob3ZlcmxhcHMpXSlcblxubG9vcC5ubyA8LSBmcmVhZChoZXJlKGNvbnNlbnN1c0RpciwgXFxjaHJvbW9fY29uc19hbm5vSGllcmFyY2h5X3BlLXBlX0E0ODV2c0RNU09fTk9fZGlmZjAuMi5iZWRwZVxcKSlcbmFuY2hvci5ubyA8LSAoZXh0cmFjdEFuY2hvcihsb29wLm5vKSlcbm92ZXJsYXBzIDwtIGZpbmRPdmVybGFwcyhhbmNob3Iubm8sIGF0YWMuZ3IpXG5hbmNob3Iubm8gPC0gcGludGVyc2VjdChhbmNob3Iubm9bcXVlcnlIaXRzKG92ZXJsYXBzKV0sIGF0YWMuZ3Jbc3ViamVjdEhpdHMob3ZlcmxhcHMpXSlcblxubG9vcC5kb3duIDwtIGZyZWFkKGhlcmUoY29uc2Vuc3VzRGlyLCBcXGNocm9tb19jb25zX2Fubm9IaWVyYXJjaHlfcGUtcGVfQTQ4NXZzRE1TT19ET1dOX2RpZmYwLjIuYmVkcGVcXCkpXG5hbmNob3IuZG93biA8LSAoZXh0cmFjdEFuY2hvcihsb29wLmRvd24pKVxub3ZlcmxhcHMgPC0gZmluZE92ZXJsYXBzKGFuY2hvci5kb3duLCBhdGFjLmdyKVxuYW5jaG9yLmRvd24gPC0gcGludGVyc2VjdChhbmNob3IuZG93bltxdWVyeUhpdHMob3ZlcmxhcHMpXSwgYXRhYy5ncltzdWJqZWN0SGl0cyhvdmVybGFwcyldKVxuXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
<!-- rnb-source-begin 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 -->
```r
```r
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
### Importing differential regulatory loops & extract anchor
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
overlaps <- findOverlaps(anchor.up, atac.gr)
anchor.up <- pintersect(anchor.up[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
overlaps <- findOverlaps(anchor.no, atac.gr)
anchor.no <- pintersect(anchor.no[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
overlaps <- findOverlaps(anchor.down, atac.gr)
anchor.down <- pintersect(anchor.down[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
```
```
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<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun                       # Use the red gradient color scale\n)\n\nfileName <- paste0(\\anchorLOLA_A485_vs_DMSO_diff0.2_regAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\UP\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n  labs(x = NULL, y = NULL)  +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\\anchorLOLA_A485_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\\)\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 4, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun # Use the red gradient color scale
)
fileName <- paste0(\anchorLOLA_A485_vs_DMSO_diff0.2_regAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Visualizing p-value and OR
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \UP\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.up, temp.down)
order <- c((temp %>% dplyr::filter(group == \UP\) %>% arrange(desc(oddsRatio)))$target,
(temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
temp$target <- factor(temp$target, levels = rev(order))
p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
geom_point() + theme_bw() + scale_color_gradient(low = \blue\, high = \red\) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- paste0(\anchorLOLA_A485_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\)
height <-3
width <- 2
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n# RUNNING LOLA\nlolaDir <- here(\\../../result/lola\\)\ndir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)\n\n# UP\nresult = runLOLA(anchor.up, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\\), sep = \\\\t\\)\n\n# NO\nresult = runLOLA(anchor.no, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\\), sep = \\\\t\\)\n\n# DOWN\nresult = runLOLA(anchor.down, anchor.all, lolaDB)\ntb = as_tibble(result)\nfwrite(tb, here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\\), sep = \\\\t\\)\n\n\n\n### HEATMAP\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody)) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio)\ncolnames(temp.up) <- c(\\target\\, \\OR_up\\)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio)\ncolnames(temp.no) <- c(\\target\\, \\OR_no\\)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio)\ncolnames(temp.down) <- c(\\target\\, \\OR_down\\)\n\n\ntemp <- full_join(full_join(temp.up, temp.no, by = c(\\target\\)), temp.down, by = c(\\target\\)) %>% mutate_all(~replace_na(., 1))\n\ndata <- as.matrix(temp[2:4])\nrownames(data) <- temp$target\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(min(data), max(data)), c(\\white\\, \\red\\))\n\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  row_km = 4,                         # Define the number of k-means clusters for rows (adjust as needed)\n  show_row_dend = FALSE,\n  col = col_fun                       # Use the red gradient color scale\n)\n\nfileName <- paste0(\\anchorLOLA_A485_vs_DMSO_diff0.2_regAnchorBackground_atac\\)\nheight <- 7\nwidth <- 3.5\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n### Visualizing p-value and OR\nalpha <- 0.05\ntb.up <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\UP\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.no <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\NO\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\ntb.down <- fread(here(lolaDir, \\LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\\)) %>%\n  dplyr::mutate(target = toupper(antibody),\n                qValueLog = -log2(qValue),\n                group = \\DOWN\\) %>%\n  filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n  dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%\n  slice_min(meanRnk, with_ties = FALSE)\n\ntemp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)\ntemp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)\n\ntemp <- bind_rows(temp.up, temp.down)\n\norder <- c((temp %>% dplyr::filter(group == \\UP\\) %>% arrange(desc(oddsRatio)))$target, \n           (temp %>% dplyr::filter(group == \\DOWN\\) %>% arrange(desc(oddsRatio)))$target)\ntemp$target <- factor(temp$target, levels = rev(order))\np <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +\n  geom_point() + theme_bw() + scale_color_gradient(low = \\blue\\, high = \\red\\) +\n  labs(x = NULL, y = NULL)  +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\nfileName <- paste0(\\anchorLOLA_A485_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\\)\nheight <-3\nwidth <- 2\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
# RUNNING LOLA
lolaDir <- here(\../../result/lola\)
dir.create(lolaDir, showWarnings = FALSE, recursive = TRUE)
# UP
result = runLOLA(anchor.up, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\), sep = \\t\)
# NO
result = runLOLA(anchor.no, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\), sep = \\t\)
# DOWN
result = runLOLA(anchor.down, anchor.all, lolaDB)
tb = as_tibble(result)
fwrite(tb, here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\), sep = \\t\)
### HEATMAP
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio)
colnames(temp.up) <- c(\target\, \OR_up\)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio)
colnames(temp.no) <- c(\target\, \OR_no\)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio)
colnames(temp.down) <- c(\target\, \OR_down\)
temp <- full_join(full_join(temp.up, temp.no, by = c(\target\)), temp.down, by = c(\target\)) %>% mutate_all(~replace_na(., 1))
data <- as.matrix(temp[2:4])
rownames(data) <- temp$target
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 4, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun # Use the red gradient color scale
)
fileName <- paste0(\anchorLOLA_A485_vs_DMSO_diff0.2_regAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Visualizing p-value and OR
alpha <- 0.05
tb.up <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_up_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \UP\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.no <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_no_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \NO\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
tb.down <- fread(here(lolaDir, \LOLA_A485_vs_DMSO_diff0.2_down_atac.tsv\)) %>%
dplyr::mutate(target = toupper(antibody),
qValueLog = -log2(qValue),
group = \DOWN\) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>% dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
temp.up <- tb.up %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.no <- tb.no %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp.down <- tb.down %>% dplyr::select(target, oddsRatio, qValueLog, group)
temp <- bind_rows(temp.up, temp.down)
order <- c((temp %>% dplyr::filter(group == \UP\) %>% arrange(desc(oddsRatio)))$target,
(temp %>% dplyr::filter(group == \DOWN\) %>% arrange(desc(oddsRatio)))$target)
temp$target <- factor(temp$target, levels = rev(order))
p <- ggplot(temp, aes(x = group, y = target, color = qValueLog, size = oddsRatio)) +
geom_point() + theme_bw() + scale_color_gradient(low = \blue\, high = \red\) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
fileName <- paste0(\anchorLOLA_A485_vs_DMSO_diff0.2_OR_qValue_regAnchorBackground_atac\)
height <-3
width <- 2
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### - Limited to ATAC-seq signal, reg loop backgrouund
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
## Importing groups
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
## Importing RNA-seq TPM for DMSO
tpm.selected <- fread(here(refDir, \readCount.filtered.TPM.all.tsv\)) %>% dplyr::select(1, 3, 4, 5)
colnames(tpm.selected) <- c(\ensembl\, \rep1\, \rep2\, \rep3\)
tpm.selected <- tpm.selected %>% dplyr::rowwise() %>%
dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group)) %>%
dplyr::mutate(avgTPM = mean(rep1, rep2, rep3))
ggplot(tpm.selected, aes(x = group, y = avgTPM)) +
geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
scale_y_log10() +
theme_classic()
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
## Importing groups
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
## Importing RNA-seq TPM for DMSO
tpm.selected <- fread(here(refDir, \readCount.filtered.TPM.all.tsv\)) %>% dplyr::select(1, 3, 4, 5)
colnames(tpm.selected) <- c(\ensembl\, \rep1\, \rep2\, \rep3\)
tpm.selected <- tpm.selected %>% dplyr::rowwise() %>%
dplyr::mutate(
group = ifelse(ensembl %in% group1, \group1\,
ifelse(ensembl %in% group2, \group2\,
ifelse(ensembl %in% group5, \group5\,
ifelse(ensembl %in% group8, \group8\, NA))))) %>%
dplyr::filter(!is.na(group)) %>%
dplyr::mutate(avgTPM = mean(rep1, rep2, rep3))
ggplot(tpm.selected, aes(x = group, y = avgTPM)) +
geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
scale_y_log10() +
theme_classic()
p <- ggplot(temp.tb, aes(x = group, y = score)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + coord_cartesian(ylim = c(ymin, ymax)) +
annotate(\text\, x = 1, y = ymin + 1, label = paste0(\p12: \, convPvalue(p12), \\n\,
\p15: \, convPvalue(p15), \\n\,
\p18: \, convPvalue(p18), \\n\,
\p25: \, convPvalue(p25), \\n\,
\p28: \, convPvalue(p28), \\n\,
\p58: \,convPvalue( p58), \\n\),
color = \black\, hjust = 0, size = 3)
fileName <- paste0(\insulation_score_\, note)
height <- 3
width <- 3
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
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cHBPdmVyMiksXG4gICAgICAgICAgIGNvdW50R2VuZUxpc3QoZ2VuZUNsdXN0ZXIsIHBwT3ZlcjEpLFxuICAgICAgICAgICBjb3VudEdlbmVMaXN0KGdlbmVDbHVzdGVyLCBwcE92ZXIwKSlcbmRhdGEgPC0gZGF0YS5mcmFtZShncm91cCwgY2x1c3RlciwgdmFsdWUpXG4jIFBsb3R0aW5nXG5nZ3Bsb3QoZGF0YSwgYWVzKGZpbGw9Y2x1c3RlciwgeT12YWx1ZSwgeD1ncm91cCkpICsgXG4gICAgZ2VvbV9iYXIocG9zaXRpb249XCJmaWxsXCIsIHN0YXQ9XCJpZGVudGl0eVwiKSArIHRoZW1lX2NsYXNzaWMoKVxuXG5gYGAifQ== -->\n\n```r\n## Importing groups\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\group1\\, 4), rep(\\group2\\, 4), rep(\\group3\\, 4), rep(\\group4\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 4)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, group1),\n           countGeneList(geneCluster, group2),\n           countGeneList(geneCluster, group5),\n           countGeneList(geneCluster, group8))\n\ndata <- data.frame(group, cluster, value)\n\n\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n# Statistical analysis \n# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group2\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group3\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group4\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n\n### P-N\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene\npnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene\npnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene\npnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene\npnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene\n\ngroup <- c(rep(\\pnOver8\\, 4), rep(\\pnOver6\\, 4), rep(\\pnOver4\\, 4), rep(\\pnOver2\\, 4), rep(\\pnOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, pnOver8),\n           countGeneList(geneCluster, pnOver6),\n           countGeneList(geneCluster, pnOver4),\n           countGeneList(geneCluster, pnOver2),\n           countGeneList(geneCluster, pnOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\ntemp <- data %>% dplyr::filter(group %in% c(\\pnOver2\\, \\pnOver8\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n### P-S\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\ngroup <- c(rep(\\psOver4\\, 4), rep(\\psOver3\\, 4), rep(\\psOver2\\, 4), rep(\\psOver1\\, 4), rep(\\psOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, psOver4),\n           countGeneList(geneCluster, psOver3),\n           countGeneList(geneCluster, psOver2),\n           countGeneList(geneCluster, psOver1),\n           countGeneList(geneCluster, psOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\ntemp <- data %>% dplyr::filter(group %in% c(\\psOver2\\, \\psOver4\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n### P-E\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\ngroup <- c(rep(\\peOver4\\, 4), rep(\\peOver3\\, 4), rep(\\peOver2\\, 4), rep(\\peOver1\\, 4), rep(\\peOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, peOver4),\n           countGeneList(geneCluster, peOver3),\n           countGeneList(geneCluster, peOver2),\n           countGeneList(geneCluster, peOver1),\n           countGeneList(geneCluster, peOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n### P-P\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\ngroup <- c(rep(\\ppOver4\\, 4), rep(\\ppOver3\\, 4), rep(\\ppOver2\\, 4), rep(\\ppOver1\\, 4), rep(\\ppOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, ppOver4),\n           countGeneList(geneCluster, ppOver3),\n           countGeneList(geneCluster, ppOver2),\n           countGeneList(geneCluster, ppOver1),\n           countGeneList(geneCluster, ppOver0))\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
## Importing groups
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
## Importing Bobbie gene classification
geneCluster <- fread(here(refDir, \bobbie_gene_classification.csv\)) %>%
dplyr::select(gene, enst, Cluster)
colnames(geneCluster) <- c(\gene\, \ensembl_transcript_id\, \cluster\)
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = geneCluster$ensembl_transcript,
mart = ensembl.v102)
geneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\ensembl_transcript_id\))
# Making data for stacked barplot
countGene <- function(geneCluster, groupName, clusterName){
num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,
cluster %in% clusterName))
return(num)
}
countGeneList <- function(geneCluster, groupName){
n1 <- countGene(geneCluster, groupName, \Early\)
n2 <- countGene(geneCluster, groupName, \Middle\)
n3 <-countGene(geneCluster, groupName, \Late\)
n4 <- countGene(geneCluster, groupName, \Transient\)
return(c(n1, n2, n3, n4))
}
group <- c(rep(\group1\, 4), rep(\group2\, 4), rep(\group3\, 4), rep(\group4\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 4)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, group1),
countGeneList(geneCluster, group2),
countGeneList(geneCluster, group5),
countGeneList(geneCluster, group8))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
# Statistical analysis
# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group2\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group3\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group4\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
### P-N
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
pnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene
pnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene
pnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene
pnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene
pnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene
group <- c(rep(\pnOver8\, 4), rep(\pnOver6\, 4), rep(\pnOver4\, 4), rep(\pnOver2\, 4), rep(\pnOver0\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 5)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, pnOver8),
countGeneList(geneCluster, pnOver6),
countGeneList(geneCluster, pnOver4),
countGeneList(geneCluster, pnOver2),
countGeneList(geneCluster, pnOver0))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
temp <- data %>% dplyr::filter(group %in% c(\pnOver2\, \pnOver8\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
### P-S
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
group <- c(rep(\psOver4\, 4), rep(\psOver3\, 4), rep(\psOver2\, 4), rep(\psOver1\, 4), rep(\psOver0\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 5)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, psOver4),
countGeneList(geneCluster, psOver3),
countGeneList(geneCluster, psOver2),
countGeneList(geneCluster, psOver1),
countGeneList(geneCluster, psOver0))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
temp <- data %>% dplyr::filter(group %in% c(\psOver2\, \psOver4\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
### P-E
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
group <- c(rep(\peOver4\, 4), rep(\peOver3\, 4), rep(\peOver2\, 4), rep(\peOver1\, 4), rep(\peOver0\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 5)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, peOver4),
countGeneList(geneCluster, peOver3),
countGeneList(geneCluster, peOver2),
countGeneList(geneCluster, peOver1),
countGeneList(geneCluster, peOver0))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
### P-P
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
group <- c(rep(\ppOver4\, 4), rep(\ppOver3\, 4), rep(\ppOver2\, 4), rep(\ppOver1\, 4), rep(\ppOver0\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 5)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, ppOver4),
countGeneList(geneCluster, ppOver3),
countGeneList(geneCluster, ppOver2),
countGeneList(geneCluster, ppOver1),
countGeneList(geneCluster, ppOver0))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
<!-- rnb-output-end -->
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c3RlciwgcHBPdmVyMiksXG4gICAgICAgICAgIGNvdW50R2VuZUxpc3QoZ2VuZUNsdXN0ZXIsIHBwT3ZlcjEpLFxuICAgICAgICAgICBjb3VudEdlbmVMaXN0KGdlbmVDbHVzdGVyLCBwcE92ZXIwKSlcbmRhdGEgPC0gZGF0YS5mcmFtZShncm91cCwgY2x1c3RlciwgdmFsdWUpXG4jIFBsb3R0aW5nXG5nZ3Bsb3QoZGF0YSwgYWVzKGZpbGw9Y2x1c3RlciwgeT12YWx1ZSwgeD1ncm91cCkpICsgXG4gICAgZ2VvbV9iYXIocG9zaXRpb249XFxmaWxsXFwsIHN0YXQ9XFxpZGVudGl0eVxcKSArIHRoZW1lX2NsYXNzaWMoKVxuXG5gYGBcbmBgYCJ9 -->\n\n```r\n```r\n## Importing groups\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\group1\\, 4), rep(\\group2\\, 4), rep(\\group3\\, 4), rep(\\group4\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 4)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, group1),\n           countGeneList(geneCluster, group2),\n           countGeneList(geneCluster, group5),\n           countGeneList(geneCluster, group8))\n\ndata <- data.frame(group, cluster, value)\n\n\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n# Statistical analysis \n# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group2\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group3\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group4\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n\n### P-N\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene\npnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene\npnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene\npnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene\npnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene\n\ngroup <- c(rep(\\pnOver8\\, 4), rep(\\pnOver6\\, 4), rep(\\pnOver4\\, 4), rep(\\pnOver2\\, 4), rep(\\pnOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, pnOver8),\n           countGeneList(geneCluster, pnOver6),\n           countGeneList(geneCluster, pnOver4),\n           countGeneList(geneCluster, pnOver2),\n           countGeneList(geneCluster, pnOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\ntemp <- data %>% dplyr::filter(group %in% c(\\pnOver2\\, \\pnOver8\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n### P-S\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\ngroup <- c(rep(\\psOver4\\, 4), rep(\\psOver3\\, 4), rep(\\psOver2\\, 4), rep(\\psOver1\\, 4), rep(\\psOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, psOver4),\n           countGeneList(geneCluster, psOver3),\n           countGeneList(geneCluster, psOver2),\n           countGeneList(geneCluster, psOver1),\n           countGeneList(geneCluster, psOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\ntemp <- data %>% dplyr::filter(group %in% c(\\psOver2\\, \\psOver4\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n### P-E\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\ngroup <- c(rep(\\peOver4\\, 4), rep(\\peOver3\\, 4), rep(\\peOver2\\, 4), rep(\\peOver1\\, 4), rep(\\peOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, peOver4),\n           countGeneList(geneCluster, peOver3),\n           countGeneList(geneCluster, peOver2),\n           countGeneList(geneCluster, peOver1),\n           countGeneList(geneCluster, peOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n### P-P\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\ngroup <- c(rep(\\ppOver4\\, 4), rep(\\ppOver3\\, 4), rep(\\ppOver2\\, 4), rep(\\ppOver1\\, 4), rep(\\ppOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, ppOver4),\n           countGeneList(geneCluster, ppOver3),\n           countGeneList(geneCluster, ppOver2),\n           countGeneList(geneCluster, ppOver1),\n           countGeneList(geneCluster, ppOver0))\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n## Importing groups\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\\))$gene\n\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\group1\\, 4), rep(\\group2\\, 4), rep(\\group3\\, 4), rep(\\group4\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 4)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, group1),\n           countGeneList(geneCluster, group2),\n           countGeneList(geneCluster, group5),\n           countGeneList(geneCluster, group8))\n\ndata <- data.frame(group, cluster, value)\n\n\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n# Statistical analysis \n# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group2\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group3\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group4\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n\n### P-N\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene\npnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene\npnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene\npnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene\npnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene\n\ngroup <- c(rep(\\pnOver8\\, 4), rep(\\pnOver6\\, 4), rep(\\pnOver4\\, 4), rep(\\pnOver2\\, 4), rep(\\pnOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, pnOver8),\n           countGeneList(geneCluster, pnOver6),\n           countGeneList(geneCluster, pnOver4),\n           countGeneList(geneCluster, pnOver2),\n           countGeneList(geneCluster, pnOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\ntemp <- data %>% dplyr::filter(group %in% c(\\pnOver2\\, \\pnOver8\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n### P-S\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\ngroup <- c(rep(\\psOver4\\, 4), rep(\\psOver3\\, 4), rep(\\psOver2\\, 4), rep(\\psOver1\\, 4), rep(\\psOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, psOver4),\n           countGeneList(geneCluster, psOver3),\n           countGeneList(geneCluster, psOver2),\n           countGeneList(geneCluster, psOver1),\n           countGeneList(geneCluster, psOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\ntemp <- data %>% dplyr::filter(group %in% c(\\psOver2\\, \\psOver4\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n### P-E\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\ngroup <- c(rep(\\peOver4\\, 4), rep(\\peOver3\\, 4), rep(\\peOver2\\, 4), rep(\\peOver1\\, 4), rep(\\peOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, peOver4),\n           countGeneList(geneCluster, peOver3),\n           countGeneList(geneCluster, peOver2),\n           countGeneList(geneCluster, peOver1),\n           countGeneList(geneCluster, peOver0))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n### P-P\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\ngroup <- c(rep(\\ppOver4\\, 4), rep(\\ppOver3\\, 4), rep(\\ppOver2\\, 4), rep(\\ppOver1\\, 4), rep(\\ppOver0\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 5)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, ppOver4),\n           countGeneList(geneCluster, ppOver3),\n           countGeneList(geneCluster, ppOver2),\n           countGeneList(geneCluster, ppOver1),\n           countGeneList(geneCluster, ppOver0))\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n```\n```"} -->
```r
```r
## Importing groups
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group2.tsv\))$gene
group5 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group5.tsv\))$gene
group8 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_group8.tsv\))$gene
## Importing Bobbie gene classification
geneCluster <- fread(here(refDir, \bobbie_gene_classification.csv\)) %>%
dplyr::select(gene, enst, Cluster)
colnames(geneCluster) <- c(\gene\, \ensembl_transcript_id\, \cluster\)
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = geneCluster$ensembl_transcript,
mart = ensembl.v102)
geneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\ensembl_transcript_id\))
# Making data for stacked barplot
countGene <- function(geneCluster, groupName, clusterName){
num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,
cluster %in% clusterName))
return(num)
}
countGeneList <- function(geneCluster, groupName){
n1 <- countGene(geneCluster, groupName, \Early\)
n2 <- countGene(geneCluster, groupName, \Middle\)
n3 <-countGene(geneCluster, groupName, \Late\)
n4 <- countGene(geneCluster, groupName, \Transient\)
return(c(n1, n2, n3, n4))
}
group <- c(rep(\group1\, 4), rep(\group2\, 4), rep(\group3\, 4), rep(\group4\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 4)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, group1),
countGeneList(geneCluster, group2),
countGeneList(geneCluster, group5),
countGeneList(geneCluster, group8))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
# Statistical analysis
# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group2\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group3\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group4\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
### P-N
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
pnOver8 <- (temp2 %>% dplyr::filter(total >= 8))$gene
pnOver6 <- (temp2 %>% dplyr::filter(total >= 6, total < 8))$gene
pnOver4 <- (temp2 %>% dplyr::filter(total >= 4, total < 6))$gene
pnOver2 <- (temp2 %>% dplyr::filter(total >= 2, total < 4))$gene
pnOver0 <- (temp2 %>% dplyr::filter(total < 2))$gene
group <- c(rep(\pnOver8\, 4), rep(\pnOver6\, 4), rep(\pnOver4\, 4), rep(\pnOver2\, 4), rep(\pnOver0\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 5)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, pnOver8),
countGeneList(geneCluster, pnOver6),
countGeneList(geneCluster, pnOver4),
countGeneList(geneCluster, pnOver2),
countGeneList(geneCluster, pnOver0))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
temp <- data %>% dplyr::filter(group %in% c(\pnOver2\, \pnOver8\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
### P-S
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
group <- c(rep(\psOver4\, 4), rep(\psOver3\, 4), rep(\psOver2\, 4), rep(\psOver1\, 4), rep(\psOver0\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 5)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, psOver4),
countGeneList(geneCluster, psOver3),
countGeneList(geneCluster, psOver2),
countGeneList(geneCluster, psOver1),
countGeneList(geneCluster, psOver0))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
temp <- data %>% dplyr::filter(group %in% c(\psOver2\, \psOver4\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
### P-E
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
group <- c(rep(\peOver4\, 4), rep(\peOver3\, 4), rep(\peOver2\, 4), rep(\peOver1\, 4), rep(\peOver0\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 5)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, peOver4),
countGeneList(geneCluster, peOver3),
countGeneList(geneCluster, peOver2),
countGeneList(geneCluster, peOver1),
countGeneList(geneCluster, peOver0))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
### P-P
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
group <- c(rep(\ppOver4\, 4), rep(\ppOver3\, 4), rep(\ppOver2\, 4), rep(\ppOver1\, 4), rep(\ppOver0\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 5)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, ppOver4),
countGeneList(geneCluster, ppOver3),
countGeneList(geneCluster, ppOver2),
countGeneList(geneCluster, ppOver1),
countGeneList(geneCluster, ppOver0))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
```
```
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## [2.22] Comparing absolute RNA expression level across group
The aim is to see if there is a trend in RNA expression level among group 1, 2, 3, 4.
It would be making comparison among genes. For this, TPM should be used.
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 -->\n\n```r\n## Importing groups\ngroup1 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup3 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group3.tsv\\))$gene\ngroup4 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group4.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup6 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group6.tsv\\))$gene\ngroup7 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group7.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group8.tsv\\))$gene\ngroup9 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group9.tsv\\))$gene\n\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\group1\\, 4), rep(\\group2\\, 4), rep(\\group3\\, 4),\n           rep(\\group4\\, 4), rep(\\group5\\, 4), rep(\\group6\\, 4),\n           rep(\\group7\\, 4), rep(\\group8\\, 4), rep(\\group9\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 9)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, group1),\n           countGeneList(geneCluster, group2),\n           countGeneList(geneCluster, group3),\n           countGeneList(geneCluster, group4),\n           countGeneList(geneCluster, group5),\n           countGeneList(geneCluster, group6),\n           countGeneList(geneCluster, group7),\n           countGeneList(geneCluster, group8),\n           countGeneList(geneCluster, group9))\n\ndata <- data.frame(group, cluster, value)\n\n\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n# Statistical analysis \n# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group2\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group3\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group4\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
## Importing groups
group1 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group2.tsv\))$gene
group3 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group3.tsv\))$gene
group4 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group4.tsv\))$gene
group5 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group5.tsv\))$gene
group6 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group6.tsv\))$gene
group7 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group7.tsv\))$gene
group8 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group8.tsv\))$gene
group9 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group9.tsv\))$gene
## Importing Bobbie gene classification
geneCluster <- fread(here(refDir, \bobbie_gene_classification.csv\)) %>%
dplyr::select(gene, enst, Cluster)
colnames(geneCluster) <- c(\gene\, \ensembl_transcript_id\, \cluster\)
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = geneCluster$ensembl_transcript,
mart = ensembl.v102)
geneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\ensembl_transcript_id\))
# Making data for stacked barplot
countGene <- function(geneCluster, groupName, clusterName){
num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,
cluster %in% clusterName))
return(num)
}
countGeneList <- function(geneCluster, groupName){
n1 <- countGene(geneCluster, groupName, \Early\)
n2 <- countGene(geneCluster, groupName, \Middle\)
n3 <-countGene(geneCluster, groupName, \Late\)
n4 <- countGene(geneCluster, groupName, \Transient\)
return(c(n1, n2, n3, n4))
}
group <- c(rep(\group1\, 4), rep(\group2\, 4), rep(\group3\, 4),
rep(\group4\, 4), rep(\group5\, 4), rep(\group6\, 4),
rep(\group7\, 4), rep(\group8\, 4), rep(\group9\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 9)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, group1),
countGeneList(geneCluster, group2),
countGeneList(geneCluster, group3),
countGeneList(geneCluster, group4),
countGeneList(geneCluster, group5),
countGeneList(geneCluster, group6),
countGeneList(geneCluster, group7),
countGeneList(geneCluster, group8),
countGeneList(geneCluster, group9))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
# Statistical analysis
# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group2\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group3\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group4\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
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-->\n\n```r\n```r\n## Importing groups\ngroup1 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group2.tsv\\))$gene\ngroup3 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group3.tsv\\))$gene\ngroup4 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group4.tsv\\))$gene\ngroup5 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group5.tsv\\))$gene\ngroup6 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group6.tsv\\))$gene\ngroup7 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group7.tsv\\))$gene\ngroup8 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group8.tsv\\))$gene\ngroup9 <- fread(here(refDir, \\geneList_A485_vs_DMSO_RNA_loop_group9.tsv\\))$gene\n\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\group1\\, 4), rep(\\group2\\, 4), rep(\\group3\\, 4),\n           rep(\\group4\\, 4), rep(\\group5\\, 4), rep(\\group6\\, 4),\n           rep(\\group7\\, 4), rep(\\group8\\, 4), rep(\\group9\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 9)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, group1),\n           countGeneList(geneCluster, group2),\n           countGeneList(geneCluster, group3),\n           countGeneList(geneCluster, group4),\n           countGeneList(geneCluster, group5),\n           countGeneList(geneCluster, group6),\n           countGeneList(geneCluster, group7),\n           countGeneList(geneCluster, group8),\n           countGeneList(geneCluster, group9))\n\ndata <- data.frame(group, cluster, value)\n\n\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n# Statistical analysis \n# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group2\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group3\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\ntemp <- data %>% dplyr::filter(group %in% c(\\group1\\, \\group4\\))\ncontingency_table <- xtabs(value ~ group + cluster, data = temp)\nfisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)\nfisher_result\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
## Importing groups
group1 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group2.tsv\))$gene
group3 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group3.tsv\))$gene
group4 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group4.tsv\))$gene
group5 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group5.tsv\))$gene
group6 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group6.tsv\))$gene
group7 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group7.tsv\))$gene
group8 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group8.tsv\))$gene
group9 <- fread(here(refDir, \geneList_A485_vs_DMSO_RNA_loop_group9.tsv\))$gene
## Importing Bobbie gene classification
geneCluster <- fread(here(refDir, \bobbie_gene_classification.csv\)) %>%
dplyr::select(gene, enst, Cluster)
colnames(geneCluster) <- c(\gene\, \ensembl_transcript_id\, \cluster\)
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = geneCluster$ensembl_transcript,
mart = ensembl.v102)
geneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\ensembl_transcript_id\))
# Making data for stacked barplot
countGene <- function(geneCluster, groupName, clusterName){
num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,
cluster %in% clusterName))
return(num)
}
countGeneList <- function(geneCluster, groupName){
n1 <- countGene(geneCluster, groupName, \Early\)
n2 <- countGene(geneCluster, groupName, \Middle\)
n3 <-countGene(geneCluster, groupName, \Late\)
n4 <- countGene(geneCluster, groupName, \Transient\)
return(c(n1, n2, n3, n4))
}
group <- c(rep(\group1\, 4), rep(\group2\, 4), rep(\group3\, 4),
rep(\group4\, 4), rep(\group5\, 4), rep(\group6\, 4),
rep(\group7\, 4), rep(\group8\, 4), rep(\group9\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 9)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, group1),
countGeneList(geneCluster, group2),
countGeneList(geneCluster, group3),
countGeneList(geneCluster, group4),
countGeneList(geneCluster, group5),
countGeneList(geneCluster, group6),
countGeneList(geneCluster, group7),
countGeneList(geneCluster, group8),
countGeneList(geneCluster, group9))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
# Statistical analysis
# Similar to the Chi-Square test, Fisher’s Exact Test is used when you have smaller sample sizes or when the expected frequency in any cell of the contingency table is below 5.
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group2\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group3\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
temp <- data %>% dplyr::filter(group %in% c(\group1\, \group4\))
contingency_table <- xtabs(value ~ group + cluster, data = temp)
fisher_result <- fisher.test(contingency_table, simulate.p.value = TRUE, B = 100000)
fisher_result
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.23] Checking Early gene from Bobbie
#### dTAG
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nextractAnchor <- function(loop){\n  anchor1 <- loop %>% dplyr::select(c(1, 2, 3))\n  colnames(anchor1) <- c(\\chr\\, \\start\\, \\end\\)\n  anchor2 <- loop %>% dplyr::select(c(4, 5, 6))\n  colnames(anchor2) <- c(\\chr\\, \\start\\, \\end\\)\n  anchors <- reduce(makeGRangesFromDataFrame(bind_rows(anchor1, anchor2)))\n    return(anchors)\n}\n\nrunFisherExact <- function(interest.gr, background.gr, query.gr){\n  overlaps_interest <- countOverlaps(interest.gr, query.gr)\n  a <- sum(overlaps_interest > 0)\n  total_interest <- length(interest.gr)\n  c <- total_interest - a\n  \n  overlaps_background <- countOverlaps(background.gr, query.gr)\n  b <- sum(overlaps_background > 0)\n  total_background <- length(background.gr)\n  d <- total_background - b\n  \n  # Construct contingency table\n  contingency_table <- matrix(c(a, c, b, d), nrow=2, byrow=TRUE,\n                              dimnames=list(\\Region\\ = c(\\interest.gr\\, \\background.gr\\),\n                                            \\Overlap\\ = c(\\Yes\\, \\No\\)))\n  \n  # Perform Fisher's exact test\n  fisher_result <- fisher.test(contingency_table)\n  \n  return(fisher_result)\n}\n\nrunFisherExactCombination <- function(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.up.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_UP\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  return(result.tb)\n}\n\nrunFisherExactCombinationBinary <- function(interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP/NO\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n\n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_UP/NO\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  return(result.tb)\n}\n\nrunFisherExactCombinationRetained <- function(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n\n  return(result.tb)\n}\n\nrunFisherExactCombinationTarget <- function(targetName, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  lost <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\oa.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(lost) <- c(\\chr\\, \\start\\, \\end\\)\n  lost.gr <- makeGRangesFromDataFrame(lost)\n  \n  result <- runFisherExactCombination(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr)\n  \n  return(result)\n}\n\nrunFisherExactCombinationTargetBinary <- function(targetName, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  lost <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\oa.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(lost) <- c(\\chr\\, \\start\\, \\end\\)\n  lost.gr <- makeGRangesFromDataFrame(lost)\n  \n  result <- runFisherExactCombinationBinary(interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr)\n  \n  return(result)\n}\n\nrunFisherExactCombinationTargetRetained <- function(targetName, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  result <- runFisherExactCombinationRetained(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr)\n  \n  return(result)\n}\n```\n\n<!-- rnb-source-end -->\n"} -->
extractAnchor <- function(loop){
anchor1 <- loop %>% dplyr::select(c(1, 2, 3))
colnames(anchor1) <- c(\chr\, \start\, \end\)
anchor2 <- loop %>% dplyr::select(c(4, 5, 6))
colnames(anchor2) <- c(\chr\, \start\, \end\)
anchors <- reduce(makeGRangesFromDataFrame(bind_rows(anchor1, anchor2)))
return(anchors)
}
runFisherExact <- function(interest.gr, background.gr, query.gr){
overlaps_interest <- countOverlaps(interest.gr, query.gr)
a <- sum(overlaps_interest > 0)
total_interest <- length(interest.gr)
c <- total_interest - a
overlaps_background <- countOverlaps(background.gr, query.gr)
b <- sum(overlaps_background > 0)
total_background <- length(background.gr)
d <- total_background - b
# Construct contingency table
contingency_table <- matrix(c(a, c, b, d), nrow=2, byrow=TRUE,
dimnames=list(\Region\ = c(\interest.gr\, \background.gr\),
\Overlap\ = c(\Yes\, \No\)))
# Perform Fisher's exact test
fisher_result <- fisher.test(contingency_table)
return(fisher_result)
}
runFisherExactCombination <- function(interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr,
targetName,
retained.gr, lost.gr){
# SEEDING
temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)
result.tb <- tibble(interest = paste0(interestName, \_UP\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_NO\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.up.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_UP\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.no.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_NO\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
return(result.tb)
}
runFisherExactCombinationBinary <- function(interestName,
loop.upno.gr, loop.down.gr,
background.gr,
targetName,
retained.gr, lost.gr){
# SEEDING
temp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)
result.tb <- tibble(interest = paste0(interestName, \_UP/NO\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_UP/NO\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
return(result.tb)
}
runFisherExactCombinationRetained <- function(interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr,
targetName,
retained.gr){
# SEEDING
temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)
result.tb <- tibble(interest = paste0(interestName, \_UP\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_NO\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
return(result.tb)
}
runFisherExactCombinationTarget <- function(targetName, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr){
# RETAINED loop
retained <- fread(here(refDir, \TF_bookmarking\, targetName, \am_a.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(retained) <- c(\chr\, \start\, \end\)
retained.gr <- makeGRangesFromDataFrame(retained)
# LOST loop
lost <- fread(here(refDir, \TF_bookmarking\, targetName, \oa.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(lost) <- c(\chr\, \start\, \end\)
lost.gr <- makeGRangesFromDataFrame(lost)
result <- runFisherExactCombination(interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr,
targetName,
retained.gr, lost.gr)
return(result)
}
runFisherExactCombinationTargetBinary <- function(targetName, interestName,
loop.upno.gr, loop.down.gr,
background.gr){
# RETAINED loop
retained <- fread(here(refDir, \TF_bookmarking\, targetName, \am_a.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(retained) <- c(\chr\, \start\, \end\)
retained.gr <- makeGRangesFromDataFrame(retained)
# LOST loop
lost <- fread(here(refDir, \TF_bookmarking\, targetName, \oa.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(lost) <- c(\chr\, \start\, \end\)
lost.gr <- makeGRangesFromDataFrame(lost)
result <- runFisherExactCombinationBinary(interestName,
loop.upno.gr, loop.down.gr,
background.gr,
targetName,
retained.gr, lost.gr)
return(result)
}
runFisherExactCombinationTargetRetained <- function(targetName, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr){
# RETAINED loop
retained <- fread(here(refDir, \TF_bookmarking\, targetName, \am_a.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(retained) <- c(\chr\, \start\, \end\)
retained.gr <- makeGRangesFromDataFrame(retained)
# LOST loop
result <- runFisherExactCombinationRetained(interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr,
targetName,
retained.gr)
return(result)
}
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nextractAnchor <- function(loop){\n  anchor1 <- loop %>% dplyr::select(c(1, 2, 3))\n  colnames(anchor1) <- c(\\chr\\, \\start\\, \\end\\)\n  anchor2 <- loop %>% dplyr::select(c(4, 5, 6))\n  colnames(anchor2) <- c(\\chr\\, \\start\\, \\end\\)\n  anchors <- reduce(makeGRangesFromDataFrame(bind_rows(anchor1, anchor2)))\n    return(anchors)\n}\n\nrunFisherExact <- function(interest.gr, background.gr, query.gr){\n  overlaps_interest <- countOverlaps(interest.gr, query.gr)\n  a <- sum(overlaps_interest > 0)\n  total_interest <- length(interest.gr)\n  c <- total_interest - a\n  \n  overlaps_background <- countOverlaps(background.gr, query.gr)\n  b <- sum(overlaps_background > 0)\n  total_background <- length(background.gr)\n  d <- total_background - b\n  \n  # Construct contingency table\n  contingency_table <- matrix(c(a, c, b, d), nrow=2, byrow=TRUE,\n                              dimnames=list(\\Region\\ = c(\\interest.gr\\, \\background.gr\\),\n                                            \\Overlap\\ = c(\\Yes\\, \\No\\)))\n  \n  # Perform Fisher's exact test\n  fisher_result <- fisher.test(contingency_table)\n  \n  return(fisher_result)\n}\n\nrunFisherExactCombination <- function(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.up.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_UP\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  return(result.tb)\n}\n\nrunFisherExactCombinationBinary <- function(interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP/NO\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n\n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_UP/NO\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  return(result.tb)\n}\n\nrunFisherExactCombinationRetained <- function(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n\n  return(result.tb)\n}\n\nrunFisherExactCombinationTarget <- function(targetName, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  lost <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\oa.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(lost) <- c(\\chr\\, \\start\\, \\end\\)\n  lost.gr <- makeGRangesFromDataFrame(lost)\n  \n  result <- runFisherExactCombination(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr)\n  \n  return(result)\n}\n\nrunFisherExactCombinationTargetBinary <- function(targetName, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  lost <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\oa.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(lost) <- c(\\chr\\, \\start\\, \\end\\)\n  lost.gr <- makeGRangesFromDataFrame(lost)\n  \n  result <- runFisherExactCombinationBinary(interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr)\n  \n  return(result)\n}\n\nrunFisherExactCombinationTargetRetained <- function(targetName, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  result <- runFisherExactCombinationRetained(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr)\n  \n  return(result)\n}\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nextractAnchor <- function(loop){\n  anchor1 <- loop %>% dplyr::select(c(1, 2, 3))\n  colnames(anchor1) <- c(\\chr\\, \\start\\, \\end\\)\n  anchor2 <- loop %>% dplyr::select(c(4, 5, 6))\n  colnames(anchor2) <- c(\\chr\\, \\start\\, \\end\\)\n  anchors <- reduce(makeGRangesFromDataFrame(bind_rows(anchor1, anchor2)))\n    return(anchors)\n}\n\nrunFisherExact <- function(interest.gr, background.gr, query.gr){\n  overlaps_interest <- countOverlaps(interest.gr, query.gr)\n  a <- sum(overlaps_interest > 0)\n  total_interest <- length(interest.gr)\n  c <- total_interest - a\n  \n  overlaps_background <- countOverlaps(background.gr, query.gr)\n  b <- sum(overlaps_background > 0)\n  total_background <- length(background.gr)\n  d <- total_background - b\n  \n  # Construct contingency table\n  contingency_table <- matrix(c(a, c, b, d), nrow=2, byrow=TRUE,\n                              dimnames=list(\\Region\\ = c(\\interest.gr\\, \\background.gr\\),\n                                            \\Overlap\\ = c(\\Yes\\, \\No\\)))\n  \n  # Perform Fisher's exact test\n  fisher_result <- fisher.test(contingency_table)\n  \n  return(fisher_result)\n}\n\nrunFisherExactCombination <- function(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.up.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_UP\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  return(result.tb)\n}\n\nrunFisherExactCombinationBinary <- function(interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP/NO\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n\n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_UP/NO\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_lost\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  return(result.tb)\n}\n\nrunFisherExactCombinationRetained <- function(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr){\n  # SEEDING\n  temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)\n  result.tb <- tibble(interest = paste0(interestName, \\_UP\\),\n                      target = paste0(targetName, \\_retained\\),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_NO\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  # ADDING ROWS\n  temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\n  result.tb <- result.tb %>% \n    add_row(interest = paste0(interestName, \\_DOWN\\),\n            target = paste0(targetName, \\_retained\\),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n\n  return(result.tb)\n}\n\nrunFisherExactCombinationTarget <- function(targetName, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  lost <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\oa.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(lost) <- c(\\chr\\, \\start\\, \\end\\)\n  lost.gr <- makeGRangesFromDataFrame(lost)\n  \n  result <- runFisherExactCombination(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr)\n  \n  return(result)\n}\n\nrunFisherExactCombinationTargetBinary <- function(targetName, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  lost <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\oa.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(lost) <- c(\\chr\\, \\start\\, \\end\\)\n  lost.gr <- makeGRangesFromDataFrame(lost)\n  \n  result <- runFisherExactCombinationBinary(interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr, lost.gr)\n  \n  return(result)\n}\n\nrunFisherExactCombinationTargetRetained <- function(targetName, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr){\n  # RETAINED loop\n  retained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\n  colnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\n  retained.gr <- makeGRangesFromDataFrame(retained)\n  # LOST loop\n  result <- runFisherExactCombinationRetained(interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr,\n                                      targetName,\n                                      retained.gr)\n  \n  return(result)\n}\n```\n```"} -->
```r
```r
extractAnchor <- function(loop){
anchor1 <- loop %>% dplyr::select(c(1, 2, 3))
colnames(anchor1) <- c(\chr\, \start\, \end\)
anchor2 <- loop %>% dplyr::select(c(4, 5, 6))
colnames(anchor2) <- c(\chr\, \start\, \end\)
anchors <- reduce(makeGRangesFromDataFrame(bind_rows(anchor1, anchor2)))
return(anchors)
}
runFisherExact <- function(interest.gr, background.gr, query.gr){
overlaps_interest <- countOverlaps(interest.gr, query.gr)
a <- sum(overlaps_interest > 0)
total_interest <- length(interest.gr)
c <- total_interest - a
overlaps_background <- countOverlaps(background.gr, query.gr)
b <- sum(overlaps_background > 0)
total_background <- length(background.gr)
d <- total_background - b
# Construct contingency table
contingency_table <- matrix(c(a, c, b, d), nrow=2, byrow=TRUE,
dimnames=list(\Region\ = c(\interest.gr\, \background.gr\),
\Overlap\ = c(\Yes\, \No\)))
# Perform Fisher's exact test
fisher_result <- fisher.test(contingency_table)
return(fisher_result)
}
runFisherExactCombination <- function(interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr,
targetName,
retained.gr, lost.gr){
# SEEDING
temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)
result.tb <- tibble(interest = paste0(interestName, \_UP\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_NO\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.up.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_UP\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.no.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_NO\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
return(result.tb)
}
runFisherExactCombinationBinary <- function(interestName,
loop.upno.gr, loop.down.gr,
background.gr,
targetName,
retained.gr, lost.gr){
# SEEDING
temp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)
result.tb <- tibble(interest = paste0(interestName, \_UP/NO\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_UP/NO\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
return(result.tb)
}
runFisherExactCombinationRetained <- function(interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr,
targetName,
retained.gr){
# SEEDING
temp <- runFisherExact(loop.up.gr, background.gr, retained.gr)
result.tb <- tibble(interest = paste0(interestName, \_UP\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.no.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_NO\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
return(result.tb)
}
runFisherExactCombinationTarget <- function(targetName, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr){
# RETAINED loop
retained <- fread(here(refDir, \TF_bookmarking\, targetName, \am_a.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(retained) <- c(\chr\, \start\, \end\)
retained.gr <- makeGRangesFromDataFrame(retained)
# LOST loop
lost <- fread(here(refDir, \TF_bookmarking\, targetName, \oa.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(lost) <- c(\chr\, \start\, \end\)
lost.gr <- makeGRangesFromDataFrame(lost)
result <- runFisherExactCombination(interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr,
targetName,
retained.gr, lost.gr)
return(result)
}
runFisherExactCombinationTargetBinary <- function(targetName, interestName,
loop.upno.gr, loop.down.gr,
background.gr){
# RETAINED loop
retained <- fread(here(refDir, \TF_bookmarking\, targetName, \am_a.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(retained) <- c(\chr\, \start\, \end\)
retained.gr <- makeGRangesFromDataFrame(retained)
# LOST loop
lost <- fread(here(refDir, \TF_bookmarking\, targetName, \oa.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(lost) <- c(\chr\, \start\, \end\)
lost.gr <- makeGRangesFromDataFrame(lost)
result <- runFisherExactCombinationBinary(interestName,
loop.upno.gr, loop.down.gr,
background.gr,
targetName,
retained.gr, lost.gr)
return(result)
}
runFisherExactCombinationTargetRetained <- function(targetName, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr){
# RETAINED loop
retained <- fread(here(refDir, \TF_bookmarking\, targetName, \am_a.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(retained) <- c(\chr\, \start\, \end\)
retained.gr <- makeGRangesFromDataFrame(retained)
# LOST loop
result <- runFisherExactCombinationRetained(interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr,
targetName,
retained.gr)
return(result)
}
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
#### A485
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n#### Importing loops of interest\ninterestName <- \\allLoop\\\n# BACKGROUND loop\nbackground <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all.bedpe\\))\nbackground.gr <- (extractAnchor(background))\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.up.gr <- (extractAnchor(loop.up))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nloop.no.gr <- (extractAnchor(loop.no))\n# DOWN loop\nloopNum <- nrow(loop.no)\nloop.all <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\))\nloop.down <- loop.all %>% dplyr::filter(diff_dTAG_DMSO < -0.2) %>% dplyr::arrange(diff_dTAG_DMSO) %>% slice_head(n = loopNum)\nloop.down.gr <- (extractAnchor(loop.down))\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\ESRRB_NCB\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\ESRRB_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\NANOG_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp11 <- runFisherExactCombinationTarget(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp12 <- runFisherExactCombinationTarget(\\OCT4_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp13 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp14 <- runFisherExactCombinationTarget(\\SOX2_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp15 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp16 <- runFisherExactCombinationTarget(\\SOX2_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp17 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10,\n         temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 5), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\, \\4\\, \\5\\)))\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of interest
interestName <- \allLoop\
# BACKGROUND loop
background <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all.bedpe\))
background.gr <- (extractAnchor(background))
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.up.gr <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\))
loop.no.gr <- (extractAnchor(loop.no))
# DOWN loop
loopNum <- nrow(loop.no)
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
loop.down <- loop.all %>% dplyr::filter(diff_dTAG_DMSO < -0.2) %>% dplyr::arrange(diff_dTAG_DMSO) %>% slice_head(n = loopNum)
loop.down.gr <- (extractAnchor(loop.down))
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\ESRRB_NCB\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\ESRRB_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\NANOG_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp11 <- runFisherExactCombinationTarget(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp12 <- runFisherExactCombinationTarget(\OCT4_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp13 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp14 <- runFisherExactCombinationTarget(\SOX2_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp15 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp16 <- runFisherExactCombinationTarget(\SOX2_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp17 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10,
temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 5),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5),
labels = c(\0\, \1\, \2\, \3\, \4\, \5\)))
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n#### Importing loops of interest\ninterestName <- \\allLoop\\\n# BACKGROUND loop\nbackground <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all.bedpe\\))\nbackground.gr <- (extractAnchor(background))\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.up.gr <- (extractAnchor(loop.up))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nloop.no.gr <- (extractAnchor(loop.no))\n# DOWN loop\nloopNum <- nrow(loop.no)\nloop.all <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\))\nloop.down <- loop.all %>% dplyr::filter(diff_dTAG_DMSO < -0.2) %>% dplyr::arrange(diff_dTAG_DMSO) %>% slice_head(n = loopNum)\nloop.down.gr <- (extractAnchor(loop.down))\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\ESRRB_NCB\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\ESRRB_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\NANOG_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp11 <- runFisherExactCombinationTarget(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp12 <- runFisherExactCombinationTarget(\\OCT4_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp13 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp14 <- runFisherExactCombinationTarget(\\SOX2_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp15 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp16 <- runFisherExactCombinationTarget(\\SOX2_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp17 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10,\n         temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 5), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\, \\4\\, \\5\\)))\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
#### Importing loops of interest
interestName <- \allLoop\
# BACKGROUND loop
background <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all.bedpe\))
background.gr <- (extractAnchor(background))
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.up.gr <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\))
loop.no.gr <- (extractAnchor(loop.no))
# DOWN loop
loopNum <- nrow(loop.no)
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
loop.down <- loop.all %>% dplyr::filter(diff_dTAG_DMSO < -0.2) %>% dplyr::arrange(diff_dTAG_DMSO) %>% slice_head(n = loopNum)
loop.down.gr <- (extractAnchor(loop.down))
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\ESRRB_NCB\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\ESRRB_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\NANOG_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp11 <- runFisherExactCombinationTarget(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp12 <- runFisherExactCombinationTarget(\OCT4_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp13 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp14 <- runFisherExactCombinationTarget(\SOX2_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp15 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp16 <- runFisherExactCombinationTarget(\SOX2_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp17 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10,
temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 5),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5),
labels = c(\0\, \1\, \2\, \3\, \4\, \5\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.24] Checking bookmarking
(1) Checking whether subset of regulatory loop have bookmarking
(2) Checking whether loops related to genes have bookmarking
H3K27ac_effie
ATAC_effie
TBP
Oct4_effie, Oct4_dsg
Sox2_effie, Sox2_dsg
Esrrb_dsg
Klf4_effie
CTCF
##### Functions
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### All loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
#### Importing loops of interest
interestName <- \regLoop\
# BACKGROUND loop
background <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
background.gr <- (extractAnchor(background))
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.up.gr <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
loop.no.gr <- (extractAnchor(loop.no))
# UPNO loop
loop.upno <- bind_rows(loop.up, loop.no)
loop.upno.gr <- extractAnchor(loop.upno)
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
loop.down.gr <- (extractAnchor(loop.down))
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
#### Importing loops of interest
interestName <- \regLoop\
# BACKGROUND loop
background <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
background.gr <- (extractAnchor(background))
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.up.gr <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
loop.no.gr <- (extractAnchor(loop.no))
# UPNO loop
loop.upno <- bind_rows(loop.up, loop.no)
loop.upno.gr <- extractAnchor(loop.upno)
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
loop.down.gr <- (extractAnchor(loop.down))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### All loops refined
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ntargetName <- \\H3K27ac_effie\\\n# RETAINED loop\nretained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\ncolnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\nretained.gr <- makeGRangesFromDataFrame(retained)\n# LOST loop\nlost <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\oa.bed\\)) %>% dplyr::select(c(1, 2, 3))\ncolnames(lost) <- c(\\chr\\, \\start\\, \\end\\)\nlost.gr <- makeGRangesFromDataFrame(lost)\n\n\n# SEEDING\ntemp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)\nresult.tb <- tibble(interest = paste0(interestName, \\_UP/NO\\),\n                    target = paste0(targetName, \\_retained\\),\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n\n# ADDING ROWS\ntemp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\nresult.tb <- result.tb %>% \n  add_row(interest = paste0(interestName, \\_DOWN\\),\n          target = paste0(targetName, \\_retained\\),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n# ADDING ROWS\ntemp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)\nresult.tb <- result.tb %>% \n  add_row(interest = paste0(interestName, \\_UP/NO\\),\n          target = paste0(targetName, \\_lost\\),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n# ADDING ROWS\ntemp <- runFisherExact(loop.down.gr, background.gr, lost.gr)\nresult.tb <- result.tb %>% \n  add_row(interest = paste0(interestName, \\_DOWN\\),\n          target = paste0(targetName, \\_lost\\),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\nresult.tb <- result.tb %>%\n  mutate(interest = recode(interest, \n                           \\regLoop_UP/NO\\ = \\UP/NO\\,\n                           \\regLoop_DOWN\\ = \\DOWN\\),\n         target = recode(target,\n                         \\H3K27ac_effie_retained\\ = \\Retained\\,\n                         \\H3K27ac_effie_lost\\ = \\Lost\\))\n\nresult.tb$interest <- factor(result.tb$interest, levels = c(\\UP/NO\\, \\DOWN\\))\n\np <- ggplot(result.tb, aes(x = interest, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 0.5*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) +\n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\heatmap_H3K27ac_bookmarking_reg_dotplot\\)\nwidth <- panelSize(1.7)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n\n  \n```\n\n<!-- rnb-source-end -->\n"} -->
targetName <- \H3K27ac_effie\
# RETAINED loop
retained <- fread(here(refDir, \TF_bookmarking\, targetName, \am_a.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(retained) <- c(\chr\, \start\, \end\)
retained.gr <- makeGRangesFromDataFrame(retained)
# LOST loop
lost <- fread(here(refDir, \TF_bookmarking\, targetName, \oa.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(lost) <- c(\chr\, \start\, \end\)
lost.gr <- makeGRangesFromDataFrame(lost)
# SEEDING
temp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)
result.tb <- tibble(interest = paste0(interestName, \_UP/NO\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_UP/NO\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
result.tb <- result.tb %>%
mutate(interest = recode(interest,
\regLoop_UP/NO\ = \UP/NO\,
\regLoop_DOWN\ = \DOWN\),
target = recode(target,
\H3K27ac_effie_retained\ = \Retained\,
\H3K27ac_effie_lost\ = \Lost\))
result.tb$interest <- factor(result.tb$interest, levels = c(\UP/NO\, \DOWN\))
p <- ggplot(result.tb, aes(x = interest, y = target, size = -log10(pvalue), fill = oddsRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 0.5*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
scale_fill_gradientn(colors = c(\#4852A0\, \white\, \#CB333A\), # Define gradient colors
values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5),
#low = \white\, high = \#CB333A\,
# limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \heatmap_H3K27ac_bookmarking_reg_dotplot\)
width <- panelSize(1.7)*mmToInch
height <- panelSize(1.1)*mmToInch
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxudGFyZ2V0TmFtZSA8LSBcXEgzSzI3YWNfZWZmaWVcXFxuIyBSRVRBSU5FRCBsb29wXG5yZXRhaW5lZCA8LSBmcmVhZChoZXJlKHJlZkRpciwgXFxURl9ib29rbWFya2luZ1xcLCB0YXJnZXROYW1lLCBcXGFtX2EuYmVkXFwpKSAlPiUgZHBseXI6OnNlbGVjdChjKDEsIDIsIDMpKVxuY29sbmFtZXMocmV0YWluZWQpIDwtIGMoXFxjaHJcXCwgXFxzdGFydFxcLCBcXGVuZFxcKVxucmV0YWluZWQuZ3IgPC0gbWFrZUdSYW5nZXNGcm9tRGF0YUZyYW1lKHJldGFpbmVkKVxuIyBMT1NUIGxvb3Bcbmxvc3QgPC0gZnJlYWQoaGVyZShyZWZEaXIsIFxcVEZfYm9va21hcmtpbmdcXCwgdGFyZ2V0TmFtZSwgXFxvYS5iZWRcXCkpICU+JSBkcGx5cjo6c2VsZWN0KGMoMSwgMiwgMykpXG5jb2xuYW1lcyhsb3N0KSA8LSBjKFxcY2hyXFwsIFxcc3RhcnRcXCwgXFxlbmRcXClcbmxvc3QuZ3IgPC0gbWFrZUdSYW5nZXNGcm9tRGF0YUZyYW1lKGxvc3QpXG5cblxuIyBTRUVESU5HXG50ZW1wIDwtIHJ1bkZpc2hlckV4YWN0KGxvb3AudXBuby5nciwgYmFja2dyb3VuZC5nciwgcmV0YWluZWQuZ3IpXG5yZXN1bHQudGIgPC0gdGliYmxlKGludGVyZXN0ID0gcGFzdGUwKGludGVyZXN0TmFtZSwgXFxfVVAvTk9cXCksXG4gICAgICAgICAgICAgICAgICAgIHRhcmdldCA9IHBhc3RlMCh0YXJnZXROYW1lLCBcXF9yZXRhaW5lZFxcKSxcbiAgICAgICAgICAgICAgICAgICAgcHZhbHVlID0gdGVtcCRwLnZhbHVlLFxuICAgICAgICAgICAgICAgICAgICBvZGRzUmF0aW8gPSB0ZW1wJGVzdGltYXRlKVxuXG4jIEFERElORyBST1dTXG50ZW1wIDwtIHJ1bkZpc2hlckV4YWN0KGxvb3AuZG93bi5nciwgYmFja2dyb3VuZC5nciwgcmV0YWluZWQuZ3IpXG5yZXN1bHQudGIgPC0gcmVzdWx0LnRiICU+JSBcbiAgYWRkX3JvdyhpbnRlcmVzdCA9IHBhc3RlMChpbnRlcmVzdE5hbWUsIFxcX0RPV05cXCksXG4gICAgICAgICAgdGFyZ2V0ID0gcGFzdGUwKHRhcmdldE5hbWUsIFxcX3JldGFpbmVkXFwpLFxuICAgICAgICAgIHB2YWx1ZSA9IHRlbXAkcC52YWx1ZSxcbiAgICAgICAgICBvZGRzUmF0aW8gPSB0ZW1wJGVzdGltYXRlKVxuXG4jIEFERElORyBST1dTXG50ZW1wIDwtIHJ1bkZpc2hlckV4YWN0KGxvb3AudXBuby5nciwgYmFja2dyb3VuZC5nciwgbG9zdC5ncilcbnJlc3VsdC50YiA8LSByZXN1bHQudGIgJT4lIFxuICBhZGRfcm93KGludGVyZXN0ID0gcGFzdGUwKGludGVyZXN0TmFtZSwgXFxfVVAvTk9cXCksXG4gICAgICAgICAgdGFyZ2V0ID0gcGFzdGUwKHRhcmdldE5hbWUsIFxcX2xvc3RcXCksXG4gICAgICAgICAgcHZhbHVlID0gdGVtcCRwLnZhbHVlLFxuICAgICAgICAgIG9kZHNSYXRpbyA9IHRlbXAkZXN0aW1hdGUpXG5cbiMgQURESU5HIFJPV1NcbnRlbXAgPC0gcnVuRmlzaGVyRXhhY3QobG9vcC5kb3duLmdyLCBiYWNrZ3JvdW5kLmdyLCBsb3N0LmdyKVxucmVzdWx0LnRiIDwtIHJlc3VsdC50YiAlPiUgXG4gIGFkZF9yb3coaW50ZXJlc3QgPSBwYXN0ZTAoaW50ZXJlc3ROYW1lLCBcXF9ET1dOXFwpLFxuICAgICAgICAgIHRhcmdldCA9IHBhc3RlMCh0YXJnZXROYW1lLCBcXF9sb3N0XFwpLFxuICAgICAgICAgIHB2YWx1ZSA9IHRlbXAkcC52YWx1ZSxcbiAgICAgICAgICBvZGRzUmF0aW8gPSB0ZW1wJGVzdGltYXRlKVxuXG5cbnJlc3VsdC50YiA8LSByZXN1bHQudGIgJT4lXG4gIG11dGF0ZShpbnRlcmVzdCA9IHJlY29kZShpbnRlcmVzdCwgXG4gICAgICAgICAgICAgICAgICAgICAgICAgICBcXHJlZ0xvb3BfVVAvTk9cXCA9IFxcVVAvTk9cXCxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgIFxccmVnTG9vcF9ET1dOXFwgPSBcXERPV05cXCksXG4gICAgICAgICB0YXJnZXQgPSByZWNvZGUodGFyZ2V0LFxuICAgICAgICAgICAgICAgICAgICAgICAgIFxcSDNLMjdhY19lZmZpZV9yZXRhaW5lZFxcID0gXFxSZXRhaW5lZFxcLFxuICAgICAgICAgICAgICAgICAgICAgICAgIFxcSDNLMjdhY19lZmZpZV9sb3N0XFwgPSBcXExvc3RcXCkpXG5cbnJlc3VsdC50YiRpbnRlcmVzdCA8LSBmYWN0b3IocmVzdWx0LnRiJGludGVyZXN0LCBsZXZlbHMgPSBjKFxcVVAvTk9cXCwgXFxET1dOXFwpKVxuXG5wIDwtIGdncGxvdChyZXN1bHQudGIsIGFlcyh4ID0gaW50ZXJlc3QsIHkgPSB0YXJnZXQsIHNpemUgPSAtbG9nMTAocHZhbHVlKSwgZmlsbCA9IG9kZHNSYXRpbykpICtcbiAgZ2VvbV9wb2ludChzaGFwZSA9IDIxLCAgICAgICAgIyBFbnN1cmVzIGEgcG9pbnQgd2l0aCBhbiBvdXRsaW5lXG4gICAgICAgICAgICAgc3Ryb2tlID0gMC41KnB0VG9NTSAgICAgICMgTGluZSB3aWR0aCBmb3IgdGhlIGJvcmRlclxuICApICsgdGhlbWVfYncoKSArIFxuICBzY2FsZV9zaXplX2NvbnRpbnVvdXMocmFuZ2UgPSBjKDEsIDMpKSArICAjIFNldCBtaW4gYW5kIG1heCBwb2ludCBzaXplcyBoZXJlXG4gIHNjYWxlX2ZpbGxfZ3JhZGllbnRuKGNvbG9ycyA9IGMoXFwjNDg1MkEwXFwsIFxcd2hpdGVcXCwgXFwjQ0IzMzNBXFwpLCAgIyBEZWZpbmUgZ3JhZGllbnQgY29sb3JzXG4gICAgICAgICAgICAgICAgICAgICAgdmFsdWVzID0gc2NhbGVzOjpyZXNjYWxlKGMoMC41LCAxLCAxLjUpKSwgbGltaXRzID0gYygwLjUsIDEuNSksIFxuICAgICAgICAgICAgICAgICAgICAgICNsb3cgPSBcXHdoaXRlXFwsIGhpZ2ggPSBcXCNDQjMzM0FcXCxcbiAgICAgICAgICAgICAgICAgICAgICAjICAgICAgICAgICAgICAgICAgbGltaXRzID0gYygxLCAzKSxcbiAgICAgICAgICAgICAgICAgICAgICBvb2IgPSBzY2FsZXM6OnNxdWlzaCwgIyBEZWZpbmUgZ3JhZGllbnQgY29sb3JzXG4gICAgICAgICAgICAgICAgICAgICAgZ3VpZGUgPSBndWlkZV9jb2xvcmJhcihcbiAgICAgICAgICAgICAgICAgICAgICAgIGJhcndpZHRoID0gMS41LzUuMDgsICAjIEFkanVzdCB3aWR0aCBvZiB0aGUgY29sb3IgYmFyXG4gICAgICAgICAgICAgICAgICAgICAgICBiYXJoZWlnaHQgPSAxNS81LjA4ICAgIyBBZGp1c3QgaGVpZ2h0IG9mIHRoZSBjb2xvciBiYXJcbiAgICAgICAgICAgICAgICAgICAgICApXG4gICkgK1xuICBsYWJzKHggPSBOVUxMLCB5ID0gTlVMTCkgICtcbiAgdGhlbWUoXG4gICAgYXhpcy50aXRsZSA9IGVsZW1lbnRfdGV4dChcbiAgICAgIHNpemUgPSBmb250U2l6ZU0sXG4gICAgICBmYW1pbHkgPSBmb250VHlwZSxcbiAgICAgIGNvbG9yID0gXFwjMDAwMDAwXFxcbiAgICApLFxuICAgIGF4aXMudGV4dCA9IGVsZW1lbnRfdGV4dChcbiAgICAgIHNpemUgPSBmb250U2l6ZU0sXG4gICAgICBmYW1pbHkgPSBmb250VHlwZSxcbiAgICAgIGNvbG9yID0gXFwjMDAwMDAwXFxcbiAgICApLFxuICAgIGF4aXMudGV4dC54ID0gZWxlbWVudF90ZXh0KFxuICAgICAgYW5nbGUgPSA0NSwgICAgICAjIFJvdGF0ZSB4LWF4aXMgbGFiZWxzIDQ1IGRlZ3JlZXNcbiAgICAgIGhqdXN0ID0gMSwgICAgICAgIyBBZGp1c3QgaG9yaXpvbnRhbCBqdXN0aWZpY2F0aW9uXG4gICAgICB2anVzdCA9IDEgICAgICAgICMgQWRqdXN0IHZlcnRpY2FsIGp1c3RpZmljYXRpb25cbiAgICApLFxuICAgIGF4aXMubGluZSA9IGVsZW1lbnRfbGluZShcbiAgICAgIGNvbG9yID0gXFwjMDAwMDAwXFwsXG4gICAgICBzaXplID0gbGluZVRoaWNrKm1tVG9MaW5lVW5pdCxcbiAgICAgIGxpbmVlbmQgPSBcXHNxdWFyZVxcXG4gICAgKSxcbiAgICBheGlzLnRpY2tzID0gZWxlbWVudF9saW5lKFxuICAgICAgY29sb3IgPSBcXCMwMDAwMDBcXCxcbiAgICAgIHNpemUgPSBsaW5lVGhpY2sqbW1Ub0xpbmVVbml0LFxuICAgICAgbGluZWVuZCA9IFxcc3F1YXJlXFxcbiAgICApLFxuICAgIHBhbmVsLmJhY2tncm91bmQgPSBlbGVtZW50X3JlY3QoZmlsbCA9IFxcdHJhbnNwYXJlbnRcXCksXG4gICAgbGVnZW5kLnRleHQgPSBlbGVtZW50X3RleHQoZmFtaWx5ID0gZm9udFR5cGUsIHNpemUgPSBmb250U2l6ZVMpLFxuICAgIGxlZ2VuZC50aXRsZSA9IGVsZW1lbnRfdGV4dChmYW1pbHkgPSBmb250VHlwZSwgc2l6ZSA9IGZvbnRTaXplUylcbiAgKVxuXG5maWxlTmFtZSA8LSBoZXJlKGZpZ0RpciwgXFxoZWF0bWFwX0gzSzI3YWNfYm9va21hcmtpbmdfcmVnX2RvdHBsb3RcXClcbndpZHRoIDwtIHBhbmVsU2l6ZSgxLjcpKm1tVG9JbmNoXG5oZWlnaHQgPC0gcGFuZWxTaXplKDEuMSkqbW1Ub0luY2hcbiMgcG5nKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnBuZ1xcKSksIHJlcyA9IDYwMCwgdW5pdCA9IFxcaW5cXCwgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxuIyBwcmludChwKVxuIyBkZXYub2ZmKClcbnN2Z2xpdGUocGFzdGUwKGZpbGVOYW1lLCBcXC5zdmdcXCksICBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG5wcmludChwKVxuZGV2Lm9mZigpXG5cbiAgXG5gYGBcbmBgYCJ9 -->\n\n```r\n```r\ntargetName <- \\H3K27ac_effie\\\n# RETAINED loop\nretained <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\am_a.bed\\)) %>% dplyr::select(c(1, 2, 3))\ncolnames(retained) <- c(\\chr\\, \\start\\, \\end\\)\nretained.gr <- makeGRangesFromDataFrame(retained)\n# LOST loop\nlost <- fread(here(refDir, \\TF_bookmarking\\, targetName, \\oa.bed\\)) %>% dplyr::select(c(1, 2, 3))\ncolnames(lost) <- c(\\chr\\, \\start\\, \\end\\)\nlost.gr <- makeGRangesFromDataFrame(lost)\n\n\n# SEEDING\ntemp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)\nresult.tb <- tibble(interest = paste0(interestName, \\_UP/NO\\),\n                    target = paste0(targetName, \\_retained\\),\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n\n# ADDING ROWS\ntemp <- runFisherExact(loop.down.gr, background.gr, retained.gr)\nresult.tb <- result.tb %>% \n  add_row(interest = paste0(interestName, \\_DOWN\\),\n          target = paste0(targetName, \\_retained\\),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n# ADDING ROWS\ntemp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)\nresult.tb <- result.tb %>% \n  add_row(interest = paste0(interestName, \\_UP/NO\\),\n          target = paste0(targetName, \\_lost\\),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n# ADDING ROWS\ntemp <- runFisherExact(loop.down.gr, background.gr, lost.gr)\nresult.tb <- result.tb %>% \n  add_row(interest = paste0(interestName, \\_DOWN\\),\n          target = paste0(targetName, \\_lost\\),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\nresult.tb <- result.tb %>%\n  mutate(interest = recode(interest, \n                           \\regLoop_UP/NO\\ = \\UP/NO\\,\n                           \\regLoop_DOWN\\ = \\DOWN\\),\n         target = recode(target,\n                         \\H3K27ac_effie_retained\\ = \\Retained\\,\n                         \\H3K27ac_effie_lost\\ = \\Lost\\))\n\nresult.tb$interest <- factor(result.tb$interest, levels = c(\\UP/NO\\, \\DOWN\\))\n\np <- ggplot(result.tb, aes(x = interest, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 0.5*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) +\n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\heatmap_H3K27ac_bookmarking_reg_dotplot\\)\nwidth <- panelSize(1.7)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n\n  \n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
targetName <- \H3K27ac_effie\
# RETAINED loop
retained <- fread(here(refDir, \TF_bookmarking\, targetName, \am_a.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(retained) <- c(\chr\, \start\, \end\)
retained.gr <- makeGRangesFromDataFrame(retained)
# LOST loop
lost <- fread(here(refDir, \TF_bookmarking\, targetName, \oa.bed\)) %>% dplyr::select(c(1, 2, 3))
colnames(lost) <- c(\chr\, \start\, \end\)
lost.gr <- makeGRangesFromDataFrame(lost)
# SEEDING
temp <- runFisherExact(loop.upno.gr, background.gr, retained.gr)
result.tb <- tibble(interest = paste0(interestName, \_UP/NO\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, retained.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_retained\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.upno.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_UP/NO\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# ADDING ROWS
temp <- runFisherExact(loop.down.gr, background.gr, lost.gr)
result.tb <- result.tb %>%
add_row(interest = paste0(interestName, \_DOWN\),
target = paste0(targetName, \_lost\),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
result.tb <- result.tb %>%
mutate(interest = recode(interest,
\regLoop_UP/NO\ = \UP/NO\,
\regLoop_DOWN\ = \DOWN\),
target = recode(target,
\H3K27ac_effie_retained\ = \Retained\,
\H3K27ac_effie_lost\ = \Lost\))
result.tb$interest <- factor(result.tb$interest, levels = c(\UP/NO\, \DOWN\))
p <- ggplot(result.tb, aes(x = interest, y = target, size = -log10(pvalue), fill = oddsRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 0.5*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
scale_fill_gradientn(colors = c(\#4852A0\, \white\, \#CB333A\), # Define gradient colors
values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5),
#low = \white\, high = \#CB333A\,
# limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \heatmap_H3K27ac_bookmarking_reg_dotplot\)
width <- panelSize(1.7)*mmToInch
height <- panelSize(1.1)*mmToInch
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### Reg loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n#### Importing loops of target\n\ntemp1 <- runFisherExactCombinationTargetBinary(\\ESRRB_dsg\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTargetBinary(\\H3K27ac_effie\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTargetBinary(\\KLF4_effie\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTargetBinary(\\OCT4_effie\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTargetBinary(\\SOX2_effie\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5)\n\n\n\nresult.tb <- data %>%\n  mutate(interest = recode(interest, \n                           \\regLoop_UP/NO\\ = \\UP/NO\\,\n                           \\regLoop_DOWN\\ = \\DOWN\\),\n         target = recode(target,\n                         \\H3K27ac_effie_retained\\ = \\H3K27ac_retained\\,\n                         \\H3K27ac_effie_lost\\ = \\H3K27ac_lost\\,\n                         \\ESRRB_dsg_retained\\ = \\ESRRB_retained\\,\n                         \\ESRRB_dsg_lost\\ = \\ESRRB_lost\\,\n                         \\KLF4_effie_retained\\ = \\KLF4_retained\\,\n                         \\KLF4_effie_lost\\ = \\KLF4_lost\\,\n                         \\OCT4_effie_retained\\ = \\OCT4_retained\\,\n                         \\OCT4_effie_lost\\ = \\OCT4_lost\\,\n                         \\SOX2_effie_retained\\ = \\SOX2_retained\\,\n                         \\SOX2_effie_lost\\ = \\SOX2_lost\\))\n\nresult.tb$interest <- factor(result.tb$interest, levels = c(\\UP/NO\\, \\DOWN\\))\nresult.tb$target <- factor(result.tb$target, levels = rev(c(\\H3K27ac_retained\\, \\H3K27ac_lost\\,\n                                                        \\ESRRB_retained\\, \\ESRRB_lost\\,\n                                                        \\KLF4_retained\\, \\KLF4_lost\\,\n                                                        \\OCT4_retained\\, \\OCT4_lost\\,\n                                                        \\SOX2_retained\\,\\SOX2_lost\\)))\n\np <- ggplot(result.tb, aes(x = interest, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) +\n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\heatmap_alltarget_bookmarking_reg_dotplot\\)\nwidth <- panelSize(2.2)*mmToInch\nheight <- panelSize(2)*mmToInch\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of target
temp1 <- runFisherExactCombinationTargetBinary(\ESRRB_dsg\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTargetBinary(\H3K27ac_effie\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTargetBinary(\KLF4_effie\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTargetBinary(\OCT4_effie\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTargetBinary(\SOX2_effie\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5)
result.tb <- data %>%
mutate(interest = recode(interest,
\regLoop_UP/NO\ = \UP/NO\,
\regLoop_DOWN\ = \DOWN\),
target = recode(target,
\H3K27ac_effie_retained\ = \H3K27ac_retained\,
\H3K27ac_effie_lost\ = \H3K27ac_lost\,
\ESRRB_dsg_retained\ = \ESRRB_retained\,
\ESRRB_dsg_lost\ = \ESRRB_lost\,
\KLF4_effie_retained\ = \KLF4_retained\,
\KLF4_effie_lost\ = \KLF4_lost\,
\OCT4_effie_retained\ = \OCT4_retained\,
\OCT4_effie_lost\ = \OCT4_lost\,
\SOX2_effie_retained\ = \SOX2_retained\,
\SOX2_effie_lost\ = \SOX2_lost\))
result.tb$interest <- factor(result.tb$interest, levels = c(\UP/NO\, \DOWN\))
result.tb$target <- factor(result.tb$target, levels = rev(c(\H3K27ac_retained\, \H3K27ac_lost\,
\ESRRB_retained\, \ESRRB_lost\,
\KLF4_retained\, \KLF4_lost\,
\OCT4_retained\, \OCT4_lost\,
\SOX2_retained\,\SOX2_lost\)))
p <- ggplot(result.tb, aes(x = interest, y = target, size = -log10(pvalue), fill = oddsRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
scale_fill_gradientn(colors = c(\#4852A0\, \white\, \#CB333A\), # Define gradient colors
values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5),
#low = \white\, high = \#CB333A\,
# limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \heatmap_alltarget_bookmarking_reg_dotplot\)
width <- panelSize(2.2)*mmToInch
height <- panelSize(2)*mmToInch
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n#### Importing loops of target\n\ntemp1 <- runFisherExactCombinationTargetBinary(\\ESRRB_dsg\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTargetBinary(\\H3K27ac_effie\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTargetBinary(\\KLF4_effie\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTargetBinary(\\OCT4_effie\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTargetBinary(\\SOX2_effie\\, interestName,\n                                      loop.upno.gr, loop.down.gr,\n                                      background.gr)\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5)\n\n\n\nresult.tb <- data %>%\n  mutate(interest = recode(interest, \n                           \\regLoop_UP/NO\\ = \\UP/NO\\,\n                           \\regLoop_DOWN\\ = \\DOWN\\),\n         target = recode(target,\n                         \\H3K27ac_effie_retained\\ = \\H3K27ac_retained\\,\n                         \\H3K27ac_effie_lost\\ = \\H3K27ac_lost\\,\n                         \\ESRRB_dsg_retained\\ = \\ESRRB_retained\\,\n                         \\ESRRB_dsg_lost\\ = \\ESRRB_lost\\,\n                         \\KLF4_effie_retained\\ = \\KLF4_retained\\,\n                         \\KLF4_effie_lost\\ = \\KLF4_lost\\,\n                         \\OCT4_effie_retained\\ = \\OCT4_retained\\,\n                         \\OCT4_effie_lost\\ = \\OCT4_lost\\,\n                         \\SOX2_effie_retained\\ = \\SOX2_retained\\,\n                         \\SOX2_effie_lost\\ = \\SOX2_lost\\))\n\nresult.tb$interest <- factor(result.tb$interest, levels = c(\\UP/NO\\, \\DOWN\\))\nresult.tb$target <- factor(result.tb$target, levels = rev(c(\\H3K27ac_retained\\, \\H3K27ac_lost\\,\n                                                        \\ESRRB_retained\\, \\ESRRB_lost\\,\n                                                        \\KLF4_retained\\, \\KLF4_lost\\,\n                                                        \\OCT4_retained\\, \\OCT4_lost\\,\n                                                        \\SOX2_retained\\,\\SOX2_lost\\)))\n\np <- ggplot(result.tb, aes(x = interest, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) +\n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\heatmap_alltarget_bookmarking_reg_dotplot\\)\nwidth <- panelSize(2.2)*mmToInch\nheight <- panelSize(2)*mmToInch\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n\n\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
#### Importing loops of target
temp1 <- runFisherExactCombinationTargetBinary(\ESRRB_dsg\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTargetBinary(\H3K27ac_effie\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTargetBinary(\KLF4_effie\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTargetBinary(\OCT4_effie\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTargetBinary(\SOX2_effie\, interestName,
loop.upno.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5)
result.tb <- data %>%
mutate(interest = recode(interest,
\regLoop_UP/NO\ = \UP/NO\,
\regLoop_DOWN\ = \DOWN\),
target = recode(target,
\H3K27ac_effie_retained\ = \H3K27ac_retained\,
\H3K27ac_effie_lost\ = \H3K27ac_lost\,
\ESRRB_dsg_retained\ = \ESRRB_retained\,
\ESRRB_dsg_lost\ = \ESRRB_lost\,
\KLF4_effie_retained\ = \KLF4_retained\,
\KLF4_effie_lost\ = \KLF4_lost\,
\OCT4_effie_retained\ = \OCT4_retained\,
\OCT4_effie_lost\ = \OCT4_lost\,
\SOX2_effie_retained\ = \SOX2_retained\,
\SOX2_effie_lost\ = \SOX2_lost\))
result.tb$interest <- factor(result.tb$interest, levels = c(\UP/NO\, \DOWN\))
result.tb$target <- factor(result.tb$target, levels = rev(c(\H3K27ac_retained\, \H3K27ac_lost\,
\ESRRB_retained\, \ESRRB_lost\,
\KLF4_retained\, \KLF4_lost\,
\OCT4_retained\, \OCT4_lost\,
\SOX2_retained\,\SOX2_lost\)))
p <- ggplot(result.tb, aes(x = interest, y = target, size = -log10(pvalue), fill = oddsRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
scale_fill_gradientn(colors = c(\#4852A0\, \white\, \#CB333A\), # Define gradient colors
values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5),
#low = \white\, high = \#CB333A\,
# limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \heatmap_alltarget_bookmarking_reg_dotplot\)
width <- panelSize(2.2)*mmToInch
height <- panelSize(2)*mmToInch
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###### Figure - H3K27ac only
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\ESRRB_NCB\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\ESRRB_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\NANOG_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp11 <- runFisherExactCombinationTarget(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp12 <- runFisherExactCombinationTarget(\\OCT4_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp13 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp14 <- runFisherExactCombinationTarget(\\SOX2_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp15 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp16 <- runFisherExactCombinationTarget(\\SOX2_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp17 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10,\n         temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 5), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\, \\4\\, \\5\\)))\n\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\ESRRB_NCB\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\ESRRB_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\NANOG_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp11 <- runFisherExactCombinationTarget(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp12 <- runFisherExactCombinationTarget(\OCT4_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp13 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp14 <- runFisherExactCombinationTarget(\SOX2_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp15 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp16 <- runFisherExactCombinationTarget(\SOX2_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp17 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10,
temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 5),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5),
labels = c(\0\, \1\, \2\, \3\, \4\, \5\)))
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\ESRRB_NCB\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\ESRRB_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\NANOG_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp11 <- runFisherExactCombinationTarget(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp12 <- runFisherExactCombinationTarget(\\OCT4_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp13 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp14 <- runFisherExactCombinationTarget(\\SOX2_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp15 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp16 <- runFisherExactCombinationTarget(\\SOX2_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp17 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10,\n         temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 5), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\, \\4\\, \\5\\)))\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\ESRRB_NCB\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\ESRRB_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\NANOG_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp11 <- runFisherExactCombinationTarget(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp12 <- runFisherExactCombinationTarget(\OCT4_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp13 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp14 <- runFisherExactCombinationTarget(\SOX2_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp15 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp16 <- runFisherExactCombinationTarget(\SOX2_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp17 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10,
temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 5),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5),
labels = c(\0\, \1\, \2\, \3\, \4\, \5\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###### Figure - all targets
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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-->\n\n```r\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTargetRetained(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTargetRetained(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTargetRetained(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTargetRetained(\\ESRRB_NCB\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTargetRetained(\\ESRRB_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTargetRetained(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTargetRetained(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTargetRetained(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTargetRetained(\\NANOG_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTargetRetained(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp11 <- runFisherExactCombinationTargetRetained(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp12 <- runFisherExactCombinationTargetRetained(\\OCT4_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp13 <- runFisherExactCombinationTargetRetained(\\SMC1\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp14 <- runFisherExactCombinationTargetRetained(\\SOX2_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp15 <- runFisherExactCombinationTargetRetained(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp16 <- runFisherExactCombinationTargetRetained(\\SOX2_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp17 <- runFisherExactCombinationTargetRetained(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10,\n         temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of target
temp1 <- runFisherExactCombinationTargetRetained(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTargetRetained(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTargetRetained(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTargetRetained(\ESRRB_NCB\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTargetRetained(\ESRRB_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTargetRetained(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTargetRetained(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTargetRetained(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTargetRetained(\NANOG_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTargetRetained(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp11 <- runFisherExactCombinationTargetRetained(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp12 <- runFisherExactCombinationTargetRetained(\OCT4_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp13 <- runFisherExactCombinationTargetRetained(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp14 <- runFisherExactCombinationTargetRetained(\SOX2_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp15 <- runFisherExactCombinationTargetRetained(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp16 <- runFisherExactCombinationTargetRetained(\SOX2_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp17 <- runFisherExactCombinationTargetRetained(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10,
temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTargetRetained(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTargetRetained(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTargetRetained(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTargetRetained(\\ESRRB_NCB\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTargetRetained(\\ESRRB_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTargetRetained(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTargetRetained(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTargetRetained(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTargetRetained(\\NANOG_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTargetRetained(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp11 <- runFisherExactCombinationTargetRetained(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp12 <- runFisherExactCombinationTargetRetained(\\OCT4_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp13 <- runFisherExactCombinationTargetRetained(\\SMC1\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp14 <- runFisherExactCombinationTargetRetained(\\SOX2_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp15 <- runFisherExactCombinationTargetRetained(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp16 <- runFisherExactCombinationTargetRetained(\\SOX2_pfa\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp17 <- runFisherExactCombinationTargetRetained(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10,\n         temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
#### Importing loops of target
temp1 <- runFisherExactCombinationTargetRetained(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTargetRetained(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTargetRetained(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTargetRetained(\ESRRB_NCB\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTargetRetained(\ESRRB_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTargetRetained(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTargetRetained(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTargetRetained(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTargetRetained(\NANOG_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTargetRetained(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp11 <- runFisherExactCombinationTargetRetained(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp12 <- runFisherExactCombinationTargetRetained(\OCT4_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp13 <- runFisherExactCombinationTargetRetained(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp14 <- runFisherExactCombinationTargetRetained(\SOX2_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp15 <- runFisherExactCombinationTargetRetained(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp16 <- runFisherExactCombinationTargetRetained(\SOX2_pfa\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp17 <- runFisherExactCombinationTargetRetained(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10,
temp11, temp12, temp13, temp14, temp15, temp16 ,temp17)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###### Retained & Lost
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###### Retained
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTargetRetained(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTargetRetained(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTargetRetained(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTargetRetained(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTargetRetained(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTargetRetained(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTargetRetained(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTargetRetained(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTargetRetained(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTargetRetained(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of target
temp1 <- runFisherExactCombinationTargetRetained(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTargetRetained(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTargetRetained(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTargetRetained(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTargetRetained(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTargetRetained(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTargetRetained(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTargetRetained(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTargetRetained(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTargetRetained(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTargetRetained(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTargetRetained(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTargetRetained(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTargetRetained(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTargetRetained(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTargetRetained(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTargetRetained(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTargetRetained(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTargetRetained(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTargetRetained(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
#### Importing loops of target
temp1 <- runFisherExactCombinationTargetRetained(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTargetRetained(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTargetRetained(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTargetRetained(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTargetRetained(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTargetRetained(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTargetRetained(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTargetRetained(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTargetRetained(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTargetRetained(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Reg refined
###### Retained & Lost
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
#### Importing loops of interest
interestName <- \allLoop\
# BACKGROUND loop
background <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all.bedpe\))
background.gr <- (extractAnchor(background))
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\))
loop.up.gr <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_NO_diff0.2.bedpe\))
loop.no.gr <- (extractAnchor(loop.no))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\))
loop.down.gr <- (extractAnchor(loop.down))
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
#### Importing loops of interest
interestName <- \allLoop\
# BACKGROUND loop
background <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all.bedpe\))
background.gr <- (extractAnchor(background))
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\))
loop.up.gr <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_NO_diff0.2.bedpe\))
loop.no.gr <- (extractAnchor(loop.no))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\))
loop.down.gr <- (extractAnchor(loop.down))
```
```
<!-- rnb-source-end -->
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<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###### Retained
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\OCT4_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\OCT4_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.24.2] Checking bookmarking - A485
#### All loops
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#### Importing loops of interest
interestName <- \regLoop\
# BACKGROUND loop
background <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
background.gr <- (extractAnchor(background))
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
loop.up.gr <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
loop.no.gr <- (extractAnchor(loop.no))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
loop.down.gr <- (extractAnchor(loop.down))
fwrite(as_tibble(loop.up.gr) %>% dplyr::select(seqnames, start, end), here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.anchor.bed\), col.names = FALSE, sep = \\t\)
fwrite(as_tibble(loop.no.gr) %>% dplyr::select(seqnames, start, end), here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.anchor.bed\), col.names = FALSE, sep = \\t\)
fwrite(as_tibble(loop.down.gr) %>% dplyr::select(seqnames, start, end), here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.anchor.bed\), col.names = FALSE, sep = \\t\)
<!-- rnb-output-end -->
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```r
```r
#### Importing loops of interest
interestName <- \regLoop\
# BACKGROUND loop
background <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
background.gr <- (extractAnchor(background))
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
loop.up.gr <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
loop.no.gr <- (extractAnchor(loop.no))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
loop.down.gr <- (extractAnchor(loop.down))
fwrite(as_tibble(loop.up.gr) %>% dplyr::select(seqnames, start, end), here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.anchor.bed\), col.names = FALSE, sep = \\t\)
fwrite(as_tibble(loop.no.gr) %>% dplyr::select(seqnames, start, end), here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.anchor.bed\), col.names = FALSE, sep = \\t\)
fwrite(as_tibble(loop.down.gr) %>% dplyr::select(seqnames, start, end), here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.anchor.bed\), col.names = FALSE, sep = \\t\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### All loops refined
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n\n\n#### Importing loops of target\ntemp1 <- runFisherExactCombinationTarget(\\ATAC_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp2 <- runFisherExactCombinationTarget(\\CTCF\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp3 <- runFisherExactCombinationTarget(\\ESRRB_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp4 <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp5 <- runFisherExactCombinationTarget(\\KLF4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp6 <- runFisherExactCombinationTarget(\\NANOG_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp7 <- runFisherExactCombinationTarget(\\OCT4_effie\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp8 <- runFisherExactCombinationTarget(\\SMC1\\, interestName,\n                                     loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp9 <- runFisherExactCombinationTarget(\\SOX2_dsg\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\ntemp10 <- runFisherExactCombinationTarget(\\TBP\\, interestName,\n                                      loop.up.gr, loop.no.gr, loop.down.gr,\n                                      background.gr)\n\n\n\n\ndata <- bind_rows(temp1, temp2, temp3, temp4, temp5,\n         temp6, temp7, temp8, temp9, temp10)\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 3), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\)))\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
#### Importing loops of target
temp1 <- runFisherExactCombinationTarget(\ATAC_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp2 <- runFisherExactCombinationTarget(\CTCF\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp3 <- runFisherExactCombinationTarget(\ESRRB_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp4 <- runFisherExactCombinationTarget(\H3K27ac_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp5 <- runFisherExactCombinationTarget(\KLF4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp6 <- runFisherExactCombinationTarget(\NANOG_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp7 <- runFisherExactCombinationTarget(\OCT4_effie\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp8 <- runFisherExactCombinationTarget(\SMC1\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp9 <- runFisherExactCombinationTarget(\SOX2_dsg\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
temp10 <- runFisherExactCombinationTarget(\TBP\, interestName,
loop.up.gr, loop.no.gr, loop.down.gr,
background.gr)
data <- bind_rows(temp1, temp2, temp3, temp4, temp5,
temp6, temp7, temp8, temp9, temp10)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 3),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3),
labels = c(\0\, \1\, \2\, \3\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Reg loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V1, TSS, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\TSS\\, \\ensembl\\)\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\n\ntemp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,\n                           end = TSS + 2500) %>%\n  dplyr::select(chr, start, end, ensembl_gene_id, diff)\n\n\ndown.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\DOWN\\))\nup.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\UP\\))\nno.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\NO\\))\nall.gr <- makeGRangesFromDataFrame(temp)\n\ndata <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, \\TSS\\,\n                                      up.gr, no.gr, down.gr,\n                                      all.gr)\n\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2), \n                                    labels = c(\\0\\, \\1\\, \\2\\)))\n\n\n## GO TEST\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\DOWN\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\n\ndownStemGene <- unique(unlist((as.data.frame(GO) %>% dplyr::slice(c(4, 5, 14, 66)) %>% dplyr::mutate(geneID = strsplit(geneID, \\/\\)))$geneID))\ndownStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(ensembl_gene_id %in% downStemGene))\n\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\UP\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\n\n\ndownNONStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\DOWN\\,\n                                                               !(ensembl_gene_id %in% downStemGene)))\n\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\UP\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\n\n\n\n### Checking bookmarking only in stem gene\n\ndata <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, \\TSS\\,\n                                      down.gr, downNONStem.gr, downStem.gr,\n                                      all.gr)\n\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2), \n                                    labels = c(\\0\\, \\1\\, \\2\\)))\n```\n\n<!-- rnb-source-end -->\n"} -->
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V1, TSS, V6)
colnames(gene.tb) <- c(\chr\, \TSS\, \ensembl\)
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
temp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,
end = TSS + 2500) %>%
dplyr::select(chr, start, end, ensembl_gene_id, diff)
down.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \DOWN\))
up.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \UP\))
no.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \NO\))
all.gr <- makeGRangesFromDataFrame(temp)
data <- runFisherExactCombinationTarget(\H3K27ac_effie\, \TSS\,
up.gr, no.gr, down.gr,
all.gr)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 2),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2),
labels = c(\0\, \1\, \2\)))
## GO TEST
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \DOWN\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
downStemGene <- unique(unlist((as.data.frame(GO) %>% dplyr::slice(c(4, 5, 14, 66)) %>% dplyr::mutate(geneID = strsplit(geneID, \/\)))$geneID))
downStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(ensembl_gene_id %in% downStemGene))
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \UP\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
downNONStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \DOWN\,
!(ensembl_gene_id %in% downStemGene)))
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \UP\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
### Checking bookmarking only in stem gene
data <- runFisherExactCombinationTarget(\H3K27ac_effie\, \TSS\,
down.gr, downNONStem.gr, downStem.gr,
all.gr)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 2),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2),
labels = c(\0\, \1\, \2\)))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V1, TSS, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\TSS\\, \\ensembl\\)\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\n\ntemp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,\n                           end = TSS + 2500) %>%\n  dplyr::select(chr, start, end, ensembl_gene_id, diff)\n\n\ndown.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\DOWN\\))\nup.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\UP\\))\nno.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\NO\\))\nall.gr <- makeGRangesFromDataFrame(temp)\n\ndata <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, \\TSS\\,\n                                      up.gr, no.gr, down.gr,\n                                      all.gr)\n\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2), \n                                    labels = c(\\0\\, \\1\\, \\2\\)))\n\n\n## GO TEST\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\DOWN\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\n\ndownStemGene <- unique(unlist((as.data.frame(GO) %>% dplyr::slice(c(4, 5, 14, 66)) %>% dplyr::mutate(geneID = strsplit(geneID, \\/\\)))$geneID))\ndownStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(ensembl_gene_id %in% downStemGene))\n\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\UP\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\n\n\ndownNONStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\DOWN\\,\n                                                               !(ensembl_gene_id %in% downStemGene)))\n\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\UP\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\n\n\n\n### Checking bookmarking only in stem gene\n\ndata <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, \\TSS\\,\n                                      down.gr, downNONStem.gr, downStem.gr,\n                                      all.gr)\n\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2), \n                                    labels = c(\\0\\, \\1\\, \\2\\)))\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V1, TSS, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\TSS\\, \\ensembl\\)\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\n\ntemp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,\n                           end = TSS + 2500) %>%\n  dplyr::select(chr, start, end, ensembl_gene_id, diff)\n\n\ndown.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\DOWN\\))\nup.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\UP\\))\nno.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\NO\\))\nall.gr <- makeGRangesFromDataFrame(temp)\n\ndata <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, \\TSS\\,\n                                      up.gr, no.gr, down.gr,\n                                      all.gr)\n\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2), \n                                    labels = c(\\0\\, \\1\\, \\2\\)))\n\n\n## GO TEST\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\DOWN\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\n\ndownStemGene <- unique(unlist((as.data.frame(GO) %>% dplyr::slice(c(4, 5, 14, 66)) %>% dplyr::mutate(geneID = strsplit(geneID, \\/\\)))$geneID))\ndownStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(ensembl_gene_id %in% downStemGene))\n\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\UP\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\n\n\ndownNONStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\DOWN\\,\n                                                               !(ensembl_gene_id %in% downStemGene)))\n\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\UP\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\n\n\n\n### Checking bookmarking only in stem gene\n\ndata <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, \\TSS\\,\n                                      down.gr, downNONStem.gr, downStem.gr,\n                                      all.gr)\n\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2), \n                                    labels = c(\\0\\, \\1\\, \\2\\)))\n```\n```"} -->
```r
```r
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V1, TSS, V6)
colnames(gene.tb) <- c(\chr\, \TSS\, \ensembl\)
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
temp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,
end = TSS + 2500) %>%
dplyr::select(chr, start, end, ensembl_gene_id, diff)
down.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \DOWN\))
up.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \UP\))
no.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \NO\))
all.gr <- makeGRangesFromDataFrame(temp)
data <- runFisherExactCombinationTarget(\H3K27ac_effie\, \TSS\,
up.gr, no.gr, down.gr,
all.gr)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 2),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2),
labels = c(\0\, \1\, \2\)))
## GO TEST
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \DOWN\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
downStemGene <- unique(unlist((as.data.frame(GO) %>% dplyr::slice(c(4, 5, 14, 66)) %>% dplyr::mutate(geneID = strsplit(geneID, \/\)))$geneID))
downStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(ensembl_gene_id %in% downStemGene))
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \UP\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
downNONStem.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \DOWN\,
!(ensembl_gene_id %in% downStemGene)))
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \UP\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
### Checking bookmarking only in stem gene
data <- runFisherExactCombinationTarget(\H3K27ac_effie\, \TSS\,
down.gr, downNONStem.gr, downStem.gr,
all.gr)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 2),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2),
labels = c(\0\, \1\, \2\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### Retained & Lost
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V1, TSS, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\TSS\\, \\ensembl\\)\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.Epi.A485_vs_G1.Epi.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\n\ntemp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,\n                           end = TSS + 2500) %>%\n  dplyr::select(chr, start, end, ensembl_gene_id, diff)\n\n\ndown.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\DOWN\\))\nup.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\UP\\))\nno.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\NO\\))\nall.gr <- makeGRangesFromDataFrame(temp)\n\ndata <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, \\TSS\\,\n                                      up.gr, no.gr, down.gr,\n                                      all.gr)\n\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2), \n                                    labels = c(\\0\\, \\1\\, \\2\\)))\n\n\n## GO\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.Epi.A485_vs_G1.Epi.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\DOWN\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\UP\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\n\n```\n\n<!-- rnb-source-end -->\n"} -->
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V1, TSS, V6)
colnames(gene.tb) <- c(\chr\, \TSS\, \ensembl\)
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.Epi.A485_vs_G1.Epi.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
temp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,
end = TSS + 2500) %>%
dplyr::select(chr, start, end, ensembl_gene_id, diff)
down.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \DOWN\))
up.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \UP\))
no.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \NO\))
all.gr <- makeGRangesFromDataFrame(temp)
data <- runFisherExactCombinationTarget(\H3K27ac_effie\, \TSS\,
up.gr, no.gr, down.gr,
all.gr)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 2),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2),
labels = c(\0\, \1\, \2\)))
## GO
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.Epi.A485_vs_G1.Epi.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \DOWN\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \UP\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
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-->\n\n```r\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V1, TSS, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\TSS\\, \\ensembl\\)\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.Epi.A485_vs_G1.Epi.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\n\ntemp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,\n                           end = TSS + 2500) %>%\n  dplyr::select(chr, start, end, ensembl_gene_id, diff)\n\n\ndown.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\DOWN\\))\nup.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\UP\\))\nno.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \\NO\\))\nall.gr <- makeGRangesFromDataFrame(temp)\n\ndata <- runFisherExactCombinationTarget(\\H3K27ac_effie\\, \\TSS\\,\n                                      up.gr, no.gr, down.gr,\n                                      all.gr)\n\n\n# Visualization\nlibrary(circlize)\n\nheatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%\npivot_wider(names_from = interest, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%\n  pivot_wider(names_from = interest, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2), \n                                    labels = c(\\0\\, \\1\\, \\2\\)))\n\n\n## GO\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.Epi.A485_vs_G1.Epi.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\DOWN\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\ngeneList <- (diff.RNA %>% dplyr::filter(diff == \\UP\\))$ensembl_gene_id\nGO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\nGO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)\nprint(dotplot(GO, showCategory = 15, title = \\\\) + \n        scale_color_continuous(limits = c(0, 0.05), low = \\red\\, high = \\black\\))\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V1, TSS, V6)
colnames(gene.tb) <- c(\chr\, \TSS\, \ensembl\)
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.Epi.A485_vs_G1.Epi.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
temp <- diff.RNA %>% dplyr::mutate(start = TSS - 2500,
end = TSS + 2500) %>%
dplyr::select(chr, start, end, ensembl_gene_id, diff)
down.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \DOWN\))
up.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \UP\))
no.gr <- makeGRangesFromDataFrame(temp %>% dplyr::filter(diff == \NO\))
all.gr <- makeGRangesFromDataFrame(temp)
data <- runFisherExactCombinationTarget(\H3K27ac_effie\, \TSS\,
up.gr, no.gr, down.gr,
all.gr)
# Visualization
library(circlize)
heatmap_data <- data %>% dplyr::select(target, interest, oddsRatio) %>%
pivot_wider(names_from = interest, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, interest, pvalue) %>%
pivot_wider(names_from = interest, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 2),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2),
labels = c(\0\, \1\, \2\)))
## GO
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.Epi.A485_vs_G1.Epi.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \DOWN\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
geneList <- (diff.RNA %>% dplyr::filter(diff == \UP\))$ensembl_gene_id
GO <- enrichGO(gene = geneList, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
GO.readable <- setReadable(GO, OrgDb = org.Mm.eg.db)
print(dotplot(GO, showCategory = 15, title = \\) +
scale_color_continuous(limits = c(0, 0.05), low = \red\, high = \black\))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### DEGs from RNA-seq
2.5kb from TSS, at least 1 bp overlap
#### 2i
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-pe_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2)\n\nid.G1SpecificPert <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\\))$V7\nid.AsyncSpecificPert <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\\))$V7\nid.bothPert <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\\))$V7\nid.bothRetained <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\\))$V7\n\n\ngeneList.G1SpecificPert <- (geneAnnoData %>% dplyr::filter(id %in% id.G1SpecificPert) %>% unnest(gene))$gene\ngeneList.AsyncSpecificPert <- (geneAnnoData %>% dplyr::filter(id %in% id.AsyncSpecificPert) %>% unnest(gene))$gene\ngeneList.bothPert <- (geneAnnoData %>% dplyr::filter(id %in% id.bothPert) %>% unnest(gene))$gene\ngeneList.bothRetained <- (geneAnnoData %>% dplyr::filter(id %in% id.bothRetained) %>% unnest(gene))$gene\n\nGO.G1SpecificPert.df <- as.data.frame(enrichGO(gene = geneList.G1SpecificPert, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO.AsyncSpecificPer.df <- as.data.frame(enrichGO(gene = geneList.AsyncSpecificPert, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO.bothPert.df <- as.data.frame(enrichGO(gene = geneList.bothPert, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO.bothRetained.df <- as.data.frame(enrichGO(gene = geneList.bothRetained, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n\nsubset.G1SpecificPert <- GO.G1SpecificPert.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\G1SpecificPert\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\nsubset.AsyncSpecificPert <- GO.AsyncSpecificPer.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\AsyncSpecificPert\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\nsubset.bothPert <- GO.bothPert.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\bothPert\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\nsubset.bothRetained <- GO.bothRetained.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\bothRetained\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\ndata <- bind_rows(bind_rows(subset.G1SpecificPert, subset.bothPert), subset.bothRetained)\n\np <- ggplot(subset.G1SpecificPert, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n  geom_point() + theme_bw() +\n  scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n  scale_size_continuous(range = c(0, 3)) +\n  labs(x = NULL, y = NULL) +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2)
id.G1SpecificPert <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\))$V7
id.AsyncSpecificPert <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\))$V7
id.bothPert <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\))$V7
id.bothRetained <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\))$V7
geneList.G1SpecificPert <- (geneAnnoData %>% dplyr::filter(id %in% id.G1SpecificPert) %>% unnest(gene))$gene
geneList.AsyncSpecificPert <- (geneAnnoData %>% dplyr::filter(id %in% id.AsyncSpecificPert) %>% unnest(gene))$gene
geneList.bothPert <- (geneAnnoData %>% dplyr::filter(id %in% id.bothPert) %>% unnest(gene))$gene
geneList.bothRetained <- (geneAnnoData %>% dplyr::filter(id %in% id.bothRetained) %>% unnest(gene))$gene
GO.G1SpecificPert.df <- as.data.frame(enrichGO(gene = geneList.G1SpecificPert, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO.AsyncSpecificPer.df <- as.data.frame(enrichGO(gene = geneList.AsyncSpecificPert, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO.bothPert.df <- as.data.frame(enrichGO(gene = geneList.bothPert, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO.bothRetained.df <- as.data.frame(enrichGO(gene = geneList.bothRetained, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
subset.G1SpecificPert <- GO.G1SpecificPert.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \G1SpecificPert\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset.AsyncSpecificPert <- GO.AsyncSpecificPer.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \AsyncSpecificPert\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset.bothPert <- GO.bothPert.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \bothPert\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset.bothRetained <- GO.bothRetained.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \bothRetained\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
data <- bind_rows(bind_rows(subset.G1SpecificPert, subset.bothPert), subset.bothRetained)
p <- ggplot(subset.G1SpecificPert, aes(x = group, y = Description, color = p.adjust, size = gr)) +
geom_point() + theme_bw() +
scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
scale_size_continuous(range = c(0, 3)) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-pe_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2)\n\nid.G1SpecificPert <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\\))$V7\nid.AsyncSpecificPert <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\\))$V7\nid.bothPert <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\\))$V7\nid.bothRetained <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\\))$V7\n\n\ngeneList.G1SpecificPert <- (geneAnnoData %>% dplyr::filter(id %in% id.G1SpecificPert) %>% unnest(gene))$gene\ngeneList.AsyncSpecificPert <- (geneAnnoData %>% dplyr::filter(id %in% id.AsyncSpecificPert) %>% unnest(gene))$gene\ngeneList.bothPert <- (geneAnnoData %>% dplyr::filter(id %in% id.bothPert) %>% unnest(gene))$gene\ngeneList.bothRetained <- (geneAnnoData %>% dplyr::filter(id %in% id.bothRetained) %>% unnest(gene))$gene\n\nGO.G1SpecificPert.df <- as.data.frame(enrichGO(gene = geneList.G1SpecificPert, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO.AsyncSpecificPer.df <- as.data.frame(enrichGO(gene = geneList.AsyncSpecificPert, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO.bothPert.df <- as.data.frame(enrichGO(gene = geneList.bothPert, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\nGO.bothRetained.df <- as.data.frame(enrichGO(gene = geneList.bothRetained, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\))\n\nsubset.G1SpecificPert <- GO.G1SpecificPert.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\G1SpecificPert\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\nsubset.AsyncSpecificPert <- GO.AsyncSpecificPer.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\AsyncSpecificPert\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\nsubset.bothPert <- GO.bothPert.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\bothPert\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\nsubset.bothRetained <- GO.bothRetained.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \\bothRetained\\) %>%\n  dplyr::mutate(\n    gr = sapply(GeneRatio, function(x) {\n      # Split the string by \\/\\\n      parts <- unlist(strsplit(x, \\/\\))\n      # Convert to numeric and perform the division\n      as.numeric(parts[1]) / as.numeric(parts[2])\n    })\n  ) %>% dplyr::arrange(desc(gr))\n\ndata <- bind_rows(bind_rows(subset.G1SpecificPert, subset.bothPert), subset.bothRetained)\n\np <- ggplot(subset.G1SpecificPert, aes(x = group, y = Description, color = p.adjust, size = gr)) + \n  geom_point() + theme_bw() +\n  scale_color_gradient(low = \\red\\, high = \\blue\\, limits = c(0, 0.05)) +\n  scale_size_continuous(range = c(0, 3)) +\n  labs(x = NULL, y = NULL) +\n  theme(axis.text = element_text(size = 6),  # Set axis text size\n        axis.title = element_text(size = 6), # Set axis title size (if not removed)\n        legend.text = element_text(size = 6), # Set legend text size\n        legend.title = element_text(size = 6)) \n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2)
id.G1SpecificPert <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_G1SpecificPert.bedpe\))$V7
id.AsyncSpecificPert <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_AsyncSpecificPert.bedpe\))$V7
id.bothPert <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothPert.bedpe\))$V7
id.bothRetained <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_G1vsAsync_bothRetained.bedpe\))$V7
geneList.G1SpecificPert <- (geneAnnoData %>% dplyr::filter(id %in% id.G1SpecificPert) %>% unnest(gene))$gene
geneList.AsyncSpecificPert <- (geneAnnoData %>% dplyr::filter(id %in% id.AsyncSpecificPert) %>% unnest(gene))$gene
geneList.bothPert <- (geneAnnoData %>% dplyr::filter(id %in% id.bothPert) %>% unnest(gene))$gene
geneList.bothRetained <- (geneAnnoData %>% dplyr::filter(id %in% id.bothRetained) %>% unnest(gene))$gene
GO.G1SpecificPert.df <- as.data.frame(enrichGO(gene = geneList.G1SpecificPert, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO.AsyncSpecificPer.df <- as.data.frame(enrichGO(gene = geneList.AsyncSpecificPert, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO.bothPert.df <- as.data.frame(enrichGO(gene = geneList.bothPert, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
GO.bothRetained.df <- as.data.frame(enrichGO(gene = geneList.bothRetained, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\))
subset.G1SpecificPert <- GO.G1SpecificPert.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \G1SpecificPert\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset.AsyncSpecificPert <- GO.AsyncSpecificPer.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \AsyncSpecificPert\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset.bothPert <- GO.bothPert.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \bothPert\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
subset.bothRetained <- GO.bothRetained.df %>% dplyr::select(ID, Description, GeneRatio, p.adjust) %>% dplyr::mutate(group = \bothRetained\) %>%
dplyr::mutate(
gr = sapply(GeneRatio, function(x) {
# Split the string by \/\
parts <- unlist(strsplit(x, \/\))
# Convert to numeric and perform the division
as.numeric(parts[1]) / as.numeric(parts[2])
})
) %>% dplyr::arrange(desc(gr))
data <- bind_rows(bind_rows(subset.G1SpecificPert, subset.bothPert), subset.bothRetained)
p <- ggplot(subset.G1SpecificPert, aes(x = group, y = Description, color = p.adjust, size = gr)) +
geom_point() + theme_bw() +
scale_color_gradient(low = \red\, high = \blue\, limits = c(0, 0.05)) +
scale_size_continuous(range = c(0, 3)) +
labs(x = NULL, y = NULL) +
theme(axis.text = element_text(size = 6), # Set axis text size
axis.title = element_text(size = 6), # Set axis title size (if not removed)
legend.text = element_text(size = 6), # Set legend text size
legend.title = element_text(size = 6))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Epi
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-pe_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2)\n\ngeneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% unnest(gene))$gene)\ngeneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% unnest(gene))$gene)\ngeneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% unnest(gene))$gene)\n\n\n\n# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN\nsets_list <- list(up = geneList.upLoop,\n                  no = geneList.noLoop,\n                  down = geneList.downLoop)\n\n# Create the Euler plot\neuler_fit <- euler(sets_list)\nplot(euler_fit,\n     labels = TRUE,  # Display set labels\n     fills = TRUE,   # Color fill the regions\n     quantities = TRUE)\n\n\n# GO\nGO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n# GO to only specific subset\ngeneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))\ngeneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))\ngeneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))\nGO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n\n## Checking percentage of bobbie early gene\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\up\\, 4), rep(\\no\\, 4), rep(\\down\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 3)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, geneList.upLoop),\n           countGeneList(geneCluster, geneList.noLoop),\n           countGeneList(geneCluster, geneList.downLoop))\n\ndata <- data.frame(group, cluster, value)\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\nvalue <- c(countGeneList(geneCluster, geneList.upLoop.specific),\n           countGeneList(geneCluster, geneList.noLoop.specific),\n           countGeneList(geneCluster, geneList.downLoop.specific))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2)
geneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \UP\) %>% unnest(gene))$gene)
geneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \NO\) %>% unnest(gene))$gene)
geneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \DOWN\) %>% unnest(gene))$gene)
# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN
sets_list <- list(up = geneList.upLoop,
no = geneList.noLoop,
down = geneList.downLoop)
# Create the Euler plot
euler_fit <- euler(sets_list)
plot(euler_fit,
labels = TRUE, # Display set labels
fills = TRUE, # Color fill the regions
quantities = TRUE)
# GO
GO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
# GO to only specific subset
geneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))
geneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))
geneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))
GO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
## Checking percentage of bobbie early gene
## Importing Bobbie gene classification
geneCluster <- fread(here(refDir, \bobbie_gene_classification.csv\)) %>%
dplyr::select(gene, enst, Cluster)
colnames(geneCluster) <- c(\gene\, \ensembl_transcript_id\, \cluster\)
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = geneCluster$ensembl_transcript,
mart = ensembl.v102)
geneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\ensembl_transcript_id\))
# Making data for stacked barplot
countGene <- function(geneCluster, groupName, clusterName){
num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,
cluster %in% clusterName))
return(num)
}
countGeneList <- function(geneCluster, groupName){
n1 <- countGene(geneCluster, groupName, \Early\)
n2 <- countGene(geneCluster, groupName, \Middle\)
n3 <-countGene(geneCluster, groupName, \Late\)
n4 <- countGene(geneCluster, groupName, \Transient\)
return(c(n1, n2, n3, n4))
}
group <- c(rep(\up\, 4), rep(\no\, 4), rep(\down\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 3)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, geneList.upLoop),
countGeneList(geneCluster, geneList.noLoop),
countGeneList(geneCluster, geneList.downLoop))
data <- data.frame(group, cluster, value)
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\stack\, stat = \identity\) + theme_classic()
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat = \identity\) + theme_classic()
value <- c(countGeneList(geneCluster, geneList.upLoop.specific),
countGeneList(geneCluster, geneList.noLoop.specific),
countGeneList(geneCluster, geneList.downLoop.specific))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\stack\, stat = \identity\) + theme_classic()
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat = \identity\) + theme_classic()
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-pe_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2)\n\ngeneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% unnest(gene))$gene)\ngeneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% unnest(gene))$gene)\ngeneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% unnest(gene))$gene)\n\n\n\n# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN\nsets_list <- list(up = geneList.upLoop,\n                  no = geneList.noLoop,\n                  down = geneList.downLoop)\n\n# Create the Euler plot\neuler_fit <- euler(sets_list)\nplot(euler_fit,\n     labels = TRUE,  # Display set labels\n     fills = TRUE,   # Color fill the regions\n     quantities = TRUE)\n\n\n# GO\nGO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n# GO to only specific subset\ngeneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))\ngeneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))\ngeneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))\nGO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n\n## Checking percentage of bobbie early gene\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\up\\, 4), rep(\\no\\, 4), rep(\\down\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 3)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, geneList.upLoop),\n           countGeneList(geneCluster, geneList.noLoop),\n           countGeneList(geneCluster, geneList.downLoop))\n\ndata <- data.frame(group, cluster, value)\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\nvalue <- c(countGeneList(geneCluster, geneList.upLoop.specific),\n           countGeneList(geneCluster, geneList.noLoop.specific),\n           countGeneList(geneCluster, geneList.downLoop.specific))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2)
geneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \UP\) %>% unnest(gene))$gene)
geneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \NO\) %>% unnest(gene))$gene)
geneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \DOWN\) %>% unnest(gene))$gene)
# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN
sets_list <- list(up = geneList.upLoop,
no = geneList.noLoop,
down = geneList.downLoop)
# Create the Euler plot
euler_fit <- euler(sets_list)
plot(euler_fit,
labels = TRUE, # Display set labels
fills = TRUE, # Color fill the regions
quantities = TRUE)
# GO
GO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
# GO to only specific subset
geneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))
geneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))
geneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))
GO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
## Checking percentage of bobbie early gene
## Importing Bobbie gene classification
geneCluster <- fread(here(refDir, \bobbie_gene_classification.csv\)) %>%
dplyr::select(gene, enst, Cluster)
colnames(geneCluster) <- c(\gene\, \ensembl_transcript_id\, \cluster\)
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = geneCluster$ensembl_transcript,
mart = ensembl.v102)
geneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\ensembl_transcript_id\))
# Making data for stacked barplot
countGene <- function(geneCluster, groupName, clusterName){
num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,
cluster %in% clusterName))
return(num)
}
countGeneList <- function(geneCluster, groupName){
n1 <- countGene(geneCluster, groupName, \Early\)
n2 <- countGene(geneCluster, groupName, \Middle\)
n3 <-countGene(geneCluster, groupName, \Late\)
n4 <- countGene(geneCluster, groupName, \Transient\)
return(c(n1, n2, n3, n4))
}
group <- c(rep(\up\, 4), rep(\no\, 4), rep(\down\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 3)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, geneList.upLoop),
countGeneList(geneCluster, geneList.noLoop),
countGeneList(geneCluster, geneList.downLoop))
data <- data.frame(group, cluster, value)
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\stack\, stat = \identity\) + theme_classic()
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat = \identity\) + theme_classic()
value <- c(countGeneList(geneCluster, geneList.upLoop.specific),
countGeneList(geneCluster, geneList.noLoop.specific),
countGeneList(geneCluster, geneList.downLoop.specific))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\stack\, stat = \identity\) + theme_classic()
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat = \identity\) + theme_classic()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.25] Checking genes related to regulatory loops
### dTAG, Comparing to Async
#### P-P, P-E
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2)\n\ngeneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% unnest(gene))$gene)\ngeneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% unnest(gene))$gene)\ngeneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% unnest(gene))$gene)\n\n\n\n# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN\nsets_list <- list(up = geneList.upLoop,\n                  no = geneList.noLoop,\n                  down = geneList.downLoop)\n\n# Create the Euler plot\neuler_fit <- euler(sets_list)\nplot(euler_fit,\n     labels = TRUE,  # Display set labels\n     fills = TRUE,   # Color fill the regions\n     quantities = TRUE)\n\n\n# GO\nGO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n# GO to only specific subset\ngeneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))\ngeneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))\ngeneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))\nGO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n\n## Checking percentage of bobbie early gene\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\up\\, 4), rep(\\no\\, 4), rep(\\down\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 3)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, geneList.upLoop),\n           countGeneList(geneCluster, geneList.noLoop),\n           countGeneList(geneCluster, geneList.downLoop))\n\ndata <- data.frame(group, cluster, value)\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\nvalue <- c(countGeneList(geneCluster, geneList.upLoop.specific),\n           countGeneList(geneCluster, geneList.noLoop.specific),\n           countGeneList(geneCluster, geneList.downLoop.specific))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\n\n\n################## Checking how those genes behave in A485\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                            padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                            TRUE ~ \\NO\\),\n                           geneGroup = case_when(ensembl_gene_id %in% geneList.upLoop ~ \\cohesin-upLoop\\,\n                                                 ensembl_gene_id %in% geneList.noLoop ~ \\cohesin-noLoop\\,\n                                                 ensembl_gene_id %in% geneList.downLoop ~ \\cohesin-downLoop\\,\n                                                 TRUE ~ \\NA\\))\n\n\nggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +\n  geom_bar(position=\\fill\\) + theme_bw()\n\n\nupLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\nupLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\n\nnoLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\nnoLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\n\ndownLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\ndownLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\n\n\ndata <- tibble(geneGroup = c(\\cohesin-upLoop\\, \\cohesin-upLoop\\, \n                     \\cohesin-noLoop\\, \\cohesin-noLoop\\, \n                     \\cohesin-downLoop\\, \\cohesin-downLoop\\),\n       diff = rep(c(\\DOWN\\, \\UP\\), 3),\n       perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)\n\nggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +\n  geom_bar(stat = \\identity\\, position = \\dodge\\) + theme_bw()\n\n\n\n\ngeneList.noLoopUnique <- geneList.noLoop[!geneList.noLoop %in% unique(c(geneList.downLoop, geneList.upLoop))]\ngeneList.downLoopUnique <- geneList.downLoop[!geneList.downLoop %in% unique(c(geneList.noLoop, geneList.upLoop))]\ngeneList.upLoopUnique <- geneList.upLoop[!geneList.upLoop %in% unique(c(geneList.noLoop, geneList.downLoop))]\n\ndiff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                            padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                            TRUE ~ \\NO\\),\n                           geneGroup = case_when(ensembl_gene_id %in% geneList.upLoopUnique  ~ \\cohesin-upLoop\\,\n                                                 ensembl_gene_id %in% geneList.noLoopUnique ~ \\cohesin-noLoop\\,\n                                                 ensembl_gene_id %in% geneList.downLoopUnique ~ \\cohesin-downLoop\\,\n                                                 TRUE ~ \\NA\\))\n\n\nggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +\n  geom_bar(position=\\fill\\) + theme_bw()\n\n\nupLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\nupLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\n\nnoLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\nnoLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\n\ndownLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\ndownLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\n\n\ndata <- tibble(geneGroup = c(\\cohesin-upLoop\\, \\cohesin-upLoop\\, \n                     \\cohesin-noLoop\\, \\cohesin-noLoop\\, \n                     \\cohesin-downLoop\\, \\cohesin-downLoop\\),\n       diff = rep(c(\\DOWN\\, \\UP\\), 3),\n       perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)\n\nggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +\n  geom_bar(stat = \\identity\\, position = \\dodge\\) + theme_bw()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2)
geneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \UP\) %>% unnest(gene))$gene)
geneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \NO\) %>% unnest(gene))$gene)
geneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \DOWN\) %>% unnest(gene))$gene)
# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN
sets_list <- list(up = geneList.upLoop,
no = geneList.noLoop,
down = geneList.downLoop)
# Create the Euler plot
euler_fit <- euler(sets_list)
plot(euler_fit,
labels = TRUE, # Display set labels
fills = TRUE, # Color fill the regions
quantities = TRUE)
# GO
GO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
# GO to only specific subset
geneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))
geneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))
geneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))
GO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
## Checking percentage of bobbie early gene
## Importing Bobbie gene classification
geneCluster <- fread(here(refDir, \bobbie_gene_classification.csv\)) %>%
dplyr::select(gene, enst, Cluster)
colnames(geneCluster) <- c(\gene\, \ensembl_transcript_id\, \cluster\)
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = geneCluster$ensembl_transcript,
mart = ensembl.v102)
geneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\ensembl_transcript_id\))
# Making data for stacked barplot
countGene <- function(geneCluster, groupName, clusterName){
num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,
cluster %in% clusterName))
return(num)
}
countGeneList <- function(geneCluster, groupName){
n1 <- countGene(geneCluster, groupName, \Early\)
n2 <- countGene(geneCluster, groupName, \Middle\)
n3 <-countGene(geneCluster, groupName, \Late\)
n4 <- countGene(geneCluster, groupName, \Transient\)
return(c(n1, n2, n3, n4))
}
group <- c(rep(\up\, 4), rep(\no\, 4), rep(\down\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 3)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, geneList.upLoop),
countGeneList(geneCluster, geneList.noLoop),
countGeneList(geneCluster, geneList.downLoop))
data <- data.frame(group, cluster, value)
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\stack\, stat = \identity\) + theme_classic()
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat = \identity\) + theme_classic()
value <- c(countGeneList(geneCluster, geneList.upLoop.specific),
countGeneList(geneCluster, geneList.noLoop.specific),
countGeneList(geneCluster, geneList.downLoop.specific))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\stack\, stat = \identity\) + theme_classic()
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat = \identity\) + theme_classic()
################## Checking how those genes behave in A485
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\),
geneGroup = case_when(ensembl_gene_id %in% geneList.upLoop ~ \cohesin-upLoop\,
ensembl_gene_id %in% geneList.noLoop ~ \cohesin-noLoop\,
ensembl_gene_id %in% geneList.downLoop ~ \cohesin-downLoop\,
TRUE ~ \NA\))
ggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +
geom_bar(position=\fill\) + theme_bw()
upLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\))
upLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\))
noLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\))
noLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\))
downLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\))
downLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\))
data <- tibble(geneGroup = c(\cohesin-upLoop\, \cohesin-upLoop\,
\cohesin-noLoop\, \cohesin-noLoop\,
\cohesin-downLoop\, \cohesin-downLoop\),
diff = rep(c(\DOWN\, \UP\), 3),
perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)
ggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +
geom_bar(stat = \identity\, position = \dodge\) + theme_bw()
geneList.noLoopUnique <- geneList.noLoop[!geneList.noLoop %in% unique(c(geneList.downLoop, geneList.upLoop))]
geneList.downLoopUnique <- geneList.downLoop[!geneList.downLoop %in% unique(c(geneList.noLoop, geneList.upLoop))]
geneList.upLoopUnique <- geneList.upLoop[!geneList.upLoop %in% unique(c(geneList.noLoop, geneList.downLoop))]
diff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\),
geneGroup = case_when(ensembl_gene_id %in% geneList.upLoopUnique ~ \cohesin-upLoop\,
ensembl_gene_id %in% geneList.noLoopUnique ~ \cohesin-noLoop\,
ensembl_gene_id %in% geneList.downLoopUnique ~ \cohesin-downLoop\,
TRUE ~ \NA\))
ggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +
geom_bar(position=\fill\) + theme_bw()
upLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\))
upLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\))
noLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\))
noLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\))
downLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\))
downLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\))
data <- tibble(geneGroup = c(\cohesin-upLoop\, \cohesin-upLoop\,
\cohesin-noLoop\, \cohesin-noLoop\,
\cohesin-downLoop\, \cohesin-downLoop\),
diff = rep(c(\DOWN\, \UP\), 3),
perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)
ggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +
geom_bar(stat = \identity\, position = \dodge\) + theme_bw()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2)\n\ngeneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% unnest(gene))$gene)\ngeneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% unnest(gene))$gene)\ngeneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% unnest(gene))$gene)\n\n\n\n# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN\nsets_list <- list(up = geneList.upLoop,\n                  no = geneList.noLoop,\n                  down = geneList.downLoop)\n\n# Create the Euler plot\neuler_fit <- euler(sets_list)\nplot(euler_fit,\n     labels = TRUE,  # Display set labels\n     fills = TRUE,   # Color fill the regions\n     quantities = TRUE)\n\n\n# GO\nGO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n# GO to only specific subset\ngeneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))\ngeneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))\ngeneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))\nGO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n\n## Checking percentage of bobbie early gene\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\up\\, 4), rep(\\no\\, 4), rep(\\down\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 3)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, geneList.upLoop),\n           countGeneList(geneCluster, geneList.noLoop),\n           countGeneList(geneCluster, geneList.downLoop))\n\ndata <- data.frame(group, cluster, value)\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\nvalue <- c(countGeneList(geneCluster, geneList.upLoop.specific),\n           countGeneList(geneCluster, geneList.noLoop.specific),\n           countGeneList(geneCluster, geneList.downLoop.specific))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\n\n\n################## Checking how those genes behave in A485\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                            padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                            TRUE ~ \\NO\\),\n                           geneGroup = case_when(ensembl_gene_id %in% geneList.upLoop ~ \\cohesin-upLoop\\,\n                                                 ensembl_gene_id %in% geneList.noLoop ~ \\cohesin-noLoop\\,\n                                                 ensembl_gene_id %in% geneList.downLoop ~ \\cohesin-downLoop\\,\n                                                 TRUE ~ \\NA\\))\n\n\nggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +\n  geom_bar(position=\\fill\\) + theme_bw()\n\n\nupLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\nupLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\n\nnoLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\nnoLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\n\ndownLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\ndownLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\n\n\ndata <- tibble(geneGroup = c(\\cohesin-upLoop\\, \\cohesin-upLoop\\, \n                     \\cohesin-noLoop\\, \\cohesin-noLoop\\, \n                     \\cohesin-downLoop\\, \\cohesin-downLoop\\),\n       diff = rep(c(\\DOWN\\, \\UP\\), 3),\n       perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)\n\nggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +\n  geom_bar(stat = \\identity\\, position = \\dodge\\) + theme_bw()\n\n\n\n\ngeneList.noLoopUnique <- geneList.noLoop[!geneList.noLoop %in% unique(c(geneList.downLoop, geneList.upLoop))]\ngeneList.downLoopUnique <- geneList.downLoop[!geneList.downLoop %in% unique(c(geneList.noLoop, geneList.upLoop))]\ngeneList.upLoopUnique <- geneList.upLoop[!geneList.upLoop %in% unique(c(geneList.noLoop, geneList.downLoop))]\n\ndiff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                            padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                            TRUE ~ \\NO\\),\n                           geneGroup = case_when(ensembl_gene_id %in% geneList.upLoopUnique  ~ \\cohesin-upLoop\\,\n                                                 ensembl_gene_id %in% geneList.noLoopUnique ~ \\cohesin-noLoop\\,\n                                                 ensembl_gene_id %in% geneList.downLoopUnique ~ \\cohesin-downLoop\\,\n                                                 TRUE ~ \\NA\\))\n\n\nggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +\n  geom_bar(position=\\fill\\) + theme_bw()\n\n\nupLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\nupLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\n\nnoLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\nnoLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\n\ndownLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\ndownLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\n\n\ndata <- tibble(geneGroup = c(\\cohesin-upLoop\\, \\cohesin-upLoop\\, \n                     \\cohesin-noLoop\\, \\cohesin-noLoop\\, \n                     \\cohesin-downLoop\\, \\cohesin-downLoop\\),\n       diff = rep(c(\\DOWN\\, \\UP\\), 3),\n       perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)\n\nggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +\n  geom_bar(stat = \\identity\\, position = \\dodge\\) + theme_bw()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2)\n\ngeneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\UP\\) %>% unnest(gene))$gene)\ngeneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\NO\\) %>% unnest(gene))$gene)\ngeneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \\DOWN\\) %>% unnest(gene))$gene)\n\n\n\n# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN\nsets_list <- list(up = geneList.upLoop,\n                  no = geneList.noLoop,\n                  down = geneList.downLoop)\n\n# Create the Euler plot\neuler_fit <- euler(sets_list)\nplot(euler_fit,\n     labels = TRUE,  # Display set labels\n     fills = TRUE,   # Color fill the regions\n     quantities = TRUE)\n\n\n# GO\nGO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n# GO to only specific subset\ngeneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))\ngeneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))\ngeneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))\nGO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.up)\nGO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.no)\nGO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \\ENSEMBL\\, ont = \\BP\\)\ndotplot(GO.down)\n\n\n## Checking percentage of bobbie early gene\n\n## Importing Bobbie gene classification\ngeneCluster <- fread(here(refDir, \\bobbie_gene_classification.csv\\)) %>%\n  dplyr::select(gene, enst, Cluster)\ncolnames(geneCluster) <- c(\\gene\\, \\ensembl_transcript_id\\, \\cluster\\)\n\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = geneCluster$ensembl_transcript,\n                 mart = ensembl.v102)\ngeneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\\ensembl_transcript_id\\))\n\n# Making data for stacked barplot\ncountGene <- function(geneCluster, groupName, clusterName){\n  num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,\n                                            cluster %in% clusterName))\n  return(num)\n}\ncountGeneList <- function(geneCluster, groupName){\n  n1 <- countGene(geneCluster, groupName, \\Early\\)\n  n2 <- countGene(geneCluster, groupName, \\Middle\\)\n  n3 <-countGene(geneCluster, groupName, \\Late\\)\n  n4 <- countGene(geneCluster, groupName, \\Transient\\)\n  return(c(n1, n2, n3, n4))\n}\n\ngroup <- c(rep(\\up\\, 4), rep(\\no\\, 4), rep(\\down\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 3)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(countGeneList(geneCluster, geneList.upLoop),\n           countGeneList(geneCluster, geneList.noLoop),\n           countGeneList(geneCluster, geneList.downLoop))\n\ndata <- data.frame(group, cluster, value)\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\nvalue <- c(countGeneList(geneCluster, geneList.upLoop.specific),\n           countGeneList(geneCluster, geneList.noLoop.specific),\n           countGeneList(geneCluster, geneList.downLoop.specific))\n\ndata <- data.frame(group, cluster, value)\n# Plotting\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\stack\\, stat = \\identity\\) + theme_classic()\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat = \\identity\\) + theme_classic()\n\n\n\n################## Checking how those genes behave in A485\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                            padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                            TRUE ~ \\NO\\),\n                           geneGroup = case_when(ensembl_gene_id %in% geneList.upLoop ~ \\cohesin-upLoop\\,\n                                                 ensembl_gene_id %in% geneList.noLoop ~ \\cohesin-noLoop\\,\n                                                 ensembl_gene_id %in% geneList.downLoop ~ \\cohesin-downLoop\\,\n                                                 TRUE ~ \\NA\\))\n\n\nggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +\n  geom_bar(position=\\fill\\) + theme_bw()\n\n\nupLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\nupLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\n\nnoLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\nnoLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\n\ndownLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\ndownLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\n\n\ndata <- tibble(geneGroup = c(\\cohesin-upLoop\\, \\cohesin-upLoop\\, \n                     \\cohesin-noLoop\\, \\cohesin-noLoop\\, \n                     \\cohesin-downLoop\\, \\cohesin-downLoop\\),\n       diff = rep(c(\\DOWN\\, \\UP\\), 3),\n       perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)\n\nggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +\n  geom_bar(stat = \\identity\\, position = \\dodge\\) + theme_bw()\n\n\n\n\ngeneList.noLoopUnique <- geneList.noLoop[!geneList.noLoop %in% unique(c(geneList.downLoop, geneList.upLoop))]\ngeneList.downLoopUnique <- geneList.downLoop[!geneList.downLoop %in% unique(c(geneList.noLoop, geneList.upLoop))]\ngeneList.upLoopUnique <- geneList.upLoop[!geneList.upLoop %in% unique(c(geneList.noLoop, geneList.downLoop))]\n\ndiff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                            padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                            TRUE ~ \\NO\\),\n                           geneGroup = case_when(ensembl_gene_id %in% geneList.upLoopUnique  ~ \\cohesin-upLoop\\,\n                                                 ensembl_gene_id %in% geneList.noLoopUnique ~ \\cohesin-noLoop\\,\n                                                 ensembl_gene_id %in% geneList.downLoopUnique ~ \\cohesin-downLoop\\,\n                                                 TRUE ~ \\NA\\))\n\n\nggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +\n  geom_bar(position=\\fill\\) + theme_bw()\n\n\nupLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\nupLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-upLoop\\))\n\nnoLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\nnoLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-noLoop\\))\n\ndownLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\DOWN\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\ndownLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\, diff == \\UP\\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \\cohesin-downLoop\\))\n\n\ndata <- tibble(geneGroup = c(\\cohesin-upLoop\\, \\cohesin-upLoop\\, \n                     \\cohesin-noLoop\\, \\cohesin-noLoop\\, \n                     \\cohesin-downLoop\\, \\cohesin-downLoop\\),\n       diff = rep(c(\\DOWN\\, \\UP\\), 3),\n       perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)\n\nggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +\n  geom_bar(stat = \\identity\\, position = \\dodge\\) + theme_bw()\n\n```\n```"} -->
```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2)
geneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \UP\) %>% unnest(gene))$gene)
geneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \NO\) %>% unnest(gene))$gene)
geneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_dTAG_DMSO == \DOWN\) %>% unnest(gene))$gene)
# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN
sets_list <- list(up = geneList.upLoop,
no = geneList.noLoop,
down = geneList.downLoop)
# Create the Euler plot
euler_fit <- euler(sets_list)
plot(euler_fit,
labels = TRUE, # Display set labels
fills = TRUE, # Color fill the regions
quantities = TRUE)
# GO
GO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
# GO to only specific subset
geneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))
geneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))
geneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))
GO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
## Checking percentage of bobbie early gene
## Importing Bobbie gene classification
geneCluster <- fread(here(refDir, \bobbie_gene_classification.csv\)) %>%
dplyr::select(gene, enst, Cluster)
colnames(geneCluster) <- c(\gene\, \ensembl_transcript_id\, \cluster\)
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = geneCluster$ensembl_transcript,
mart = ensembl.v102)
geneCluster <- geneCluster %>% dplyr::left_join(idPair_tg, by = c(\ensembl_transcript_id\))
# Making data for stacked barplot
countGene <- function(geneCluster, groupName, clusterName){
num <- nrow(geneCluster %>% dplyr::filter(ensembl_gene_id %in% groupName,
cluster %in% clusterName))
return(num)
}
countGeneList <- function(geneCluster, groupName){
n1 <- countGene(geneCluster, groupName, \Early\)
n2 <- countGene(geneCluster, groupName, \Middle\)
n3 <-countGene(geneCluster, groupName, \Late\)
n4 <- countGene(geneCluster, groupName, \Transient\)
return(c(n1, n2, n3, n4))
}
group <- c(rep(\up\, 4), rep(\no\, 4), rep(\down\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 3)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(countGeneList(geneCluster, geneList.upLoop),
countGeneList(geneCluster, geneList.noLoop),
countGeneList(geneCluster, geneList.downLoop))
data <- data.frame(group, cluster, value)
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\stack\, stat = \identity\) + theme_classic()
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat = \identity\) + theme_classic()
value <- c(countGeneList(geneCluster, geneList.upLoop.specific),
countGeneList(geneCluster, geneList.noLoop.specific),
countGeneList(geneCluster, geneList.downLoop.specific))
data <- data.frame(group, cluster, value)
# Plotting
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\stack\, stat = \identity\) + theme_classic()
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat = \identity\) + theme_classic()
################## Checking how those genes behave in A485
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\),
geneGroup = case_when(ensembl_gene_id %in% geneList.upLoop ~ \cohesin-upLoop\,
ensembl_gene_id %in% geneList.noLoop ~ \cohesin-noLoop\,
ensembl_gene_id %in% geneList.downLoop ~ \cohesin-downLoop\,
TRUE ~ \NA\))
ggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +
geom_bar(position=\fill\) + theme_bw()
upLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\))
upLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\))
noLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\))
noLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\))
downLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\))
downLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\))
data <- tibble(geneGroup = c(\cohesin-upLoop\, \cohesin-upLoop\,
\cohesin-noLoop\, \cohesin-noLoop\,
\cohesin-downLoop\, \cohesin-downLoop\),
diff = rep(c(\DOWN\, \UP\), 3),
perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)
ggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +
geom_bar(stat = \identity\, position = \dodge\) + theme_bw()
geneList.noLoopUnique <- geneList.noLoop[!geneList.noLoop %in% unique(c(geneList.downLoop, geneList.upLoop))]
geneList.downLoopUnique <- geneList.downLoop[!geneList.downLoop %in% unique(c(geneList.noLoop, geneList.upLoop))]
geneList.upLoopUnique <- geneList.upLoop[!geneList.upLoop %in% unique(c(geneList.noLoop, geneList.downLoop))]
diff.RNA.out <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\),
geneGroup = case_when(ensembl_gene_id %in% geneList.upLoopUnique ~ \cohesin-upLoop\,
ensembl_gene_id %in% geneList.noLoopUnique ~ \cohesin-noLoop\,
ensembl_gene_id %in% geneList.downLoopUnique ~ \cohesin-downLoop\,
TRUE ~ \NA\))
ggplot(diff.RNA.out, aes(x = geneGroup, fill = diff)) +
geom_bar(position=\fill\) + theme_bw()
upLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\))
upLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-upLoop\))
noLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\))
noLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-noLoop\))
downLoop.downPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\, diff == \DOWN\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\))
downLoop.upPerc <- nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\, diff == \UP\))/nrow(diff.RNA.out %>% dplyr::filter(geneGroup == \cohesin-downLoop\))
data <- tibble(geneGroup = c(\cohesin-upLoop\, \cohesin-upLoop\,
\cohesin-noLoop\, \cohesin-noLoop\,
\cohesin-downLoop\, \cohesin-downLoop\),
diff = rep(c(\DOWN\, \UP\), 3),
perc = c(upLoop.downPerc, upLoop.upPerc, noLoop.downPerc, noLoop.upPerc, downLoop.downPerc, downLoop.upPerc)*100)
ggplot(data, aes(x = geneGroup, y = perc, fill = diff )) +
geom_bar(stat = \identity\, position = \dodge\) + theme_bw()
```
```
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### dTAG
#### P-P, P-E
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<!-- rnb-output-begin 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 -->
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2)
geneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \UP\) %>% unnest(gene))$gene)
geneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \NO\) %>% unnest(gene))$gene)
geneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \DOWN\) %>% unnest(gene))$gene)
# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN
sets_list <- list(up = geneList.upLoop,
no = geneList.noLoop,
down = geneList.downLoop)
# Create the Euler plot
euler_fit <- euler(sets_list)
plot(euler_fit,
labels = TRUE, # Display set labels
fills = TRUE, # Color fill the regions
quantities = TRUE)
# GO
GO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up, showCategory = 15)
GO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no, showCategory = 15)
GO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down, showCategory = 20)
# GO to only specific subset
geneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))
geneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))
geneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))
GO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down, showCategory = 20)
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<!-- rnb-output-begin 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-->
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```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2)
geneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \UP\) %>% unnest(gene))$gene)
geneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \NO\) %>% unnest(gene))$gene)
geneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \DOWN\) %>% unnest(gene))$gene)
# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN
sets_list <- list(up = geneList.upLoop,
no = geneList.noLoop,
down = geneList.downLoop)
# Create the Euler plot
euler_fit <- euler(sets_list)
plot(euler_fit,
labels = TRUE, # Display set labels
fills = TRUE, # Color fill the regions
quantities = TRUE)
# GO
GO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up, showCategory = 15)
GO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no, showCategory = 15)
GO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down, showCategory = 20)
# GO to only specific subset
geneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))
geneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))
geneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))
GO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down, showCategory = 20)
```
```
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#### P-N
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name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2)
geneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \UP\) %>% unnest(gene))$gene)
geneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \NO\) %>% unnest(gene))$gene)
geneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \DOWN\) %>% unnest(gene))$gene)
# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN
sets_list <- list(up = geneList.upLoop,
no = geneList.noLoop,
down = geneList.downLoop)
# Create the Euler plot
euler_fit <- euler(sets_list)
plot(euler_fit,
labels = TRUE, # Display set labels
fills = TRUE, # Color fill the regions
quantities = TRUE)
# GO
GO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down, showCategory = 20)
# GO to only specific subset
geneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))
geneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))
geneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))
GO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
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-->
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```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2)
geneList.upLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \UP\) %>% unnest(gene))$gene)
geneList.noLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \NO\) %>% unnest(gene))$gene)
geneList.downLoop <- unique((geneAnnoData %>% dplyr::filter(updown_A485_DMSO == \DOWN\) %>% unnest(gene))$gene)
# CHECKING HOW MANY GENES OVERLAP AMONG UP/NO/DOWN
sets_list <- list(up = geneList.upLoop,
no = geneList.noLoop,
down = geneList.downLoop)
# Create the Euler plot
euler_fit <- euler(sets_list)
plot(euler_fit,
labels = TRUE, # Display set labels
fills = TRUE, # Color fill the regions
quantities = TRUE)
# GO
GO.up <- enrichGO(gene = geneList.upLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down, showCategory = 20)
# GO to only specific subset
geneList.upLoop.specific <- setdiff(geneList.upLoop, union(geneList.noLoop, geneList.downLoop))
geneList.noLoop.specific <- setdiff(geneList.noLoop, union(geneList.upLoop, geneList.downLoop))
geneList.downLoop.specific <- setdiff(geneList.downLoop, union(geneList.noLoop, geneList.upLoop))
GO.up <- enrichGO(gene = geneList.upLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.up)
GO.no <- enrichGO(gene = geneList.noLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.no)
GO.down <- enrichGO(gene = geneList.downLoop.specific, OrgDb = org.Mm.eg.db, keyType = \ENSEMBL\, ont = \BP\)
dotplot(GO.down)
```
```
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### A485
#### P-P, P-E
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-->\n\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore\n  distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nname <- \\chromo_cons_annoHierarchy\\\n\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nanchor.up <- (extractAnchor(loop.up))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.no <- (extractAnchor(loop.no))\n# UP NO\nloop.upno <- bind_rows(loop.up, loop.no)\nanchor.upno <- (extractAnchor(loop.upno))\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- (extractAnchor(loop.down))\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScores <- function(track, peak, name){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor.up)\n  b <- getSumScores(peakTrack, anchor.no)\n  c <- getSumScores(peakTrack, anchor.down)\n  a.tb <- tibble(loop = \\UP\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\NO\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\DOWN\\,\n                 sumScore = c)\n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\ average peak score per anchor\\)) +\n    geom_violin(aes(fill = group), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) +\n    coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.95))) + theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n}\n\nplotSumScoresBinary <- function(track, peak, name, anchor.upno, anchor.down){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  b <- getSumScores(peakTrack, anchor.upno)\n  c <- getSumScores(peakTrack, anchor.down)\n  b.tb <- tibble(loop = \\UP/NO\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\DOWN\\,\n                 sumScore = c)\n  data <- bind_rows(b.tb, c.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\UP/NO\\, \\DOWN\\))\n  \n  p12 <- getPvalWilcox(data, \\UP/NO\\, \\DOWN\\)\n  p <-  ggplot(data, aes(x = name, fill = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(\\Average ChIP peak score at anchor)\\)) +\n    introdataviz::geom_split_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                                    alpha = .4) +\n    geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 0,\n      ),\n      axis.text.y = element_text(\n        size = fontSizeS,\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.9))) + \n    annotate(\n     \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n     label = paste0(\\p12: \\, convPvalue(p12)),\n     color = \\black\\, hjust = 0, size = 3\n   ) +   guides(\n    fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n    )\n  )\n  \n   fileName <- paste0(\\ChIP_peak_avgPeakScore_\\, name)\n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\H3K4me3\\)\nplotSumScoresBinary(track, peak, \\H3K4me3\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n#plotSumScores(track, peak, \\H3K27ac\\)\nplotSumScoresBinary(track, peak, \\H3K27ac\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\n#plotSumScores(track, peak, \\H3K14ac\\)\nplotSumScoresBinary(track, peak, \\H3K14ac\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\CTCF\\)\nplotSumScoresBinary(track, peak, \\CTCF\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\RAD21\\)\nplotSumScoresBinary(track, peak, \\RAD21\\, anchor.upno, anchor.down)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore
distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
# UP NO
loop.upno <- bind_rows(loop.up, loop.no)
anchor.upno <- (extractAnchor(loop.upno))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
getSumScores <- function(track, anchor) {
# Find overlaps between all anchors and track regions at once
overlaps <- findOverlaps(anchor, track)
# Extract the scores and corresponding anchor indices
anchor_indices <- queryHits(overlaps)
track_scores <- score(track)[subjectHits(overlaps)]
# Use tapply to calculate the median scores for each anchor
median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)
# Initialize a numeric vector to store the median scores for each anchor
all_median_scores <- rep(NA, length(anchor))
# Populate the median scores for the anchors that have overlaps
all_median_scores[as.numeric(names(median_scores))] <- median_scores
return(all_median_scores)
}
plotSumScores <- function(track, peak, name){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
a <- getSumScores(peakTrack, anchor.up)
b <- getSumScores(peakTrack, anchor.no)
c <- getSumScores(peakTrack, anchor.down)
a.tb <- tibble(loop = \UP\,
sumScore = a)
b.tb <- tibble(loop = \NO\,
sumScore = b)
c.tb <- tibble(loop = \DOWN\,
sumScore = c)
data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()
ggplot(data, aes(x = loop, y = sumScore)) +
labs(x = NULL, y = paste0(name, \ average peak score per anchor\)) +
geom_violin(aes(fill = group), color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.size = 1, outlier.stroke = NA) + theme_classic() +
stat_summary(
aes(group = loop), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.95))) + theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
}
plotSumScoresBinary <- function(track, peak, name, anchor.upno, anchor.down){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
b <- getSumScores(peakTrack, anchor.upno)
c <- getSumScores(peakTrack, anchor.down)
b.tb <- tibble(loop = \UP/NO\,
sumScore = b)
c.tb <- tibble(loop = \DOWN\,
sumScore = c)
data <- bind_rows(b.tb, c.tb) %>% drop_na()
data$loop <- factor(data$loop, levels = c(\UP/NO\, \DOWN\))
p12 <- getPvalWilcox(data, \UP/NO\, \DOWN\)
p <- ggplot(data, aes(x = name, fill = loop, y = sumScore)) +
labs(x = NULL, y = paste0(\Average ChIP peak score at anchor)\)) +
introdataviz::geom_split_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\,
alpha = .4) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) + theme_classic() +
stat_summary(
aes(group = loop), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) + theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 0,
),
axis.text.y = element_text(
size = fontSizeS,
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.9))) +
annotate(
\text\, x = 1, y = quantile(data$sumScore, 0.5),
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
) + guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
)
)
fileName <- paste0(\ChIP_peak_avgPeakScore_\, name)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# Sum peak score
####
track <- import(here(refDir, \33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \H3K4me3\)
plotSumScoresBinary(track, peak, \H3K4me3\, anchor.upno, anchor.down)
track <- import(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
#plotSumScores(track, peak, \H3K27ac\)
plotSumScoresBinary(track, peak, \H3K27ac\, anchor.upno, anchor.down)
track <- import(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.bed\))
#plotSumScores(track, peak, \H3K14ac\)
plotSumScoresBinary(track, peak, \H3K14ac\, anchor.upno, anchor.down)
track <- import(here(refDir, \33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \CTCF\)
plotSumScoresBinary(track, peak, \CTCF\, anchor.upno, anchor.down)
track <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \RAD21\)
plotSumScoresBinary(track, peak, \RAD21\, anchor.upno, anchor.down)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore\n  distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nname <- \\chromo_cons_annoHierarchy\\\n\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nanchor.up <- (extractAnchor(loop.up))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.no <- (extractAnchor(loop.no))\n# UP NO\nloop.upno <- bind_rows(loop.up, loop.no)\nanchor.upno <- (extractAnchor(loop.upno))\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- (extractAnchor(loop.down))\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScores <- function(track, peak, name){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor.up)\n  b <- getSumScores(peakTrack, anchor.no)\n  c <- getSumScores(peakTrack, anchor.down)\n  a.tb <- tibble(loop = \\UP\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\NO\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\DOWN\\,\n                 sumScore = c)\n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\ average peak score per anchor\\)) +\n    geom_violin(aes(fill = group), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) +\n    coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.95))) + theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n}\n\nplotSumScoresBinary <- function(track, peak, name, anchor.upno, anchor.down){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  b <- getSumScores(peakTrack, anchor.upno)\n  c <- getSumScores(peakTrack, anchor.down)\n  b.tb <- tibble(loop = \\UP/NO\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\DOWN\\,\n                 sumScore = c)\n  data <- bind_rows(b.tb, c.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\UP/NO\\, \\DOWN\\))\n  \n  p12 <- getPvalWilcox(data, \\UP/NO\\, \\DOWN\\)\n  p <-  ggplot(data, aes(x = name, fill = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(\\Average ChIP peak score at anchor)\\)) +\n    introdataviz::geom_split_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                                    alpha = .4) +\n    geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 0,\n      ),\n      axis.text.y = element_text(\n        size = fontSizeS,\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.9))) + \n    annotate(\n     \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n     label = paste0(\\p12: \\, convPvalue(p12)),\n     color = \\black\\, hjust = 0, size = 3\n   ) +   guides(\n    fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n    )\n  )\n  \n   fileName <- paste0(\\ChIP_peak_avgPeakScore_\\, name)\n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\H3K4me3\\)\nplotSumScoresBinary(track, peak, \\H3K4me3\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n#plotSumScores(track, peak, \\H3K27ac\\)\nplotSumScoresBinary(track, peak, \\H3K27ac\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\n#plotSumScores(track, peak, \\H3K14ac\\)\nplotSumScoresBinary(track, peak, \\H3K14ac\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\CTCF\\)\nplotSumScoresBinary(track, peak, \\CTCF\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\RAD21\\)\nplotSumScoresBinary(track, peak, \\RAD21\\, anchor.upno, anchor.down)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore\n  distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nname <- \\chromo_cons_annoHierarchy\\\n\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nanchor.up <- (extractAnchor(loop.up))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.no <- (extractAnchor(loop.no))\n# UP NO\nloop.upno <- bind_rows(loop.up, loop.no)\nanchor.upno <- (extractAnchor(loop.upno))\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- (extractAnchor(loop.down))\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScores <- function(track, peak, name){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor.up)\n  b <- getSumScores(peakTrack, anchor.no)\n  c <- getSumScores(peakTrack, anchor.down)\n  a.tb <- tibble(loop = \\UP\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\NO\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\DOWN\\,\n                 sumScore = c)\n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\ average peak score per anchor\\)) +\n    geom_violin(aes(fill = group), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) +\n    coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.95))) + theme(\n    axis.title = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n}\n\nplotSumScoresBinary <- function(track, peak, name, anchor.upno, anchor.down){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  b <- getSumScores(peakTrack, anchor.upno)\n  c <- getSumScores(peakTrack, anchor.down)\n  b.tb <- tibble(loop = \\UP/NO\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\DOWN\\,\n                 sumScore = c)\n  data <- bind_rows(b.tb, c.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\UP/NO\\, \\DOWN\\))\n  \n  p12 <- getPvalWilcox(data, \\UP/NO\\, \\DOWN\\)\n  p <-  ggplot(data, aes(x = name, fill = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(\\Average ChIP peak score at anchor)\\)) +\n    introdataviz::geom_split_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                                    alpha = .4) +\n    geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeM, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 0,\n      ),\n      axis.text.y = element_text(\n        size = fontSizeS,\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.9))) + \n    annotate(\n     \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n     label = paste0(\\p12: \\, convPvalue(p12)),\n     color = \\black\\, hjust = 0, size = 3\n   ) +   guides(\n    fill = guide_legend(\n        keywidth = 0.2,  # Adjust the width of the legend keys\n        keyheight = 0.2  # Adjust the height of the legend keys\n    )\n  )\n  \n   fileName <- paste0(\\ChIP_peak_avgPeakScore_\\, name)\n  width <- panelSize(1.5)*mmToInch\n  height <- panelSize(1.2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\H3K4me3\\)\nplotSumScoresBinary(track, peak, \\H3K4me3\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n#plotSumScores(track, peak, \\H3K27ac\\)\nplotSumScoresBinary(track, peak, \\H3K27ac\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\n#plotSumScores(track, peak, \\H3K14ac\\)\nplotSumScoresBinary(track, peak, \\H3K14ac\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\CTCF\\)\nplotSumScoresBinary(track, peak, \\CTCF\\, anchor.upno, anchor.down)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\RAD21\\)\nplotSumScoresBinary(track, peak, \\RAD21\\, anchor.upno, anchor.down)\n\n\n```\n```"} -->
```r
```r
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore
distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
# UP NO
loop.upno <- bind_rows(loop.up, loop.no)
anchor.upno <- (extractAnchor(loop.upno))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
getSumScores <- function(track, anchor) {
# Find overlaps between all anchors and track regions at once
overlaps <- findOverlaps(anchor, track)
# Extract the scores and corresponding anchor indices
anchor_indices <- queryHits(overlaps)
track_scores <- score(track)[subjectHits(overlaps)]
# Use tapply to calculate the median scores for each anchor
median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)
# Initialize a numeric vector to store the median scores for each anchor
all_median_scores <- rep(NA, length(anchor))
# Populate the median scores for the anchors that have overlaps
all_median_scores[as.numeric(names(median_scores))] <- median_scores
return(all_median_scores)
}
plotSumScores <- function(track, peak, name){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
a <- getSumScores(peakTrack, anchor.up)
b <- getSumScores(peakTrack, anchor.no)
c <- getSumScores(peakTrack, anchor.down)
a.tb <- tibble(loop = \UP\,
sumScore = a)
b.tb <- tibble(loop = \NO\,
sumScore = b)
c.tb <- tibble(loop = \DOWN\,
sumScore = c)
data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()
ggplot(data, aes(x = loop, y = sumScore)) +
labs(x = NULL, y = paste0(name, \ average peak score per anchor\)) +
geom_violin(aes(fill = group), color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.size = 1, outlier.stroke = NA) + theme_classic() +
stat_summary(
aes(group = loop), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) +
coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.95))) + theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
}
plotSumScoresBinary <- function(track, peak, name, anchor.upno, anchor.down){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
b <- getSumScores(peakTrack, anchor.upno)
c <- getSumScores(peakTrack, anchor.down)
b.tb <- tibble(loop = \UP/NO\,
sumScore = b)
c.tb <- tibble(loop = \DOWN\,
sumScore = c)
data <- bind_rows(b.tb, c.tb) %>% drop_na()
data$loop <- factor(data$loop, levels = c(\UP/NO\, \DOWN\))
p12 <- getPvalWilcox(data, \UP/NO\, \DOWN\)
p <- ggplot(data, aes(x = name, fill = loop, y = sumScore)) +
labs(x = NULL, y = paste0(\Average ChIP peak score at anchor)\)) +
introdataviz::geom_split_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\,
alpha = .4) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) + theme_classic() +
stat_summary(
aes(group = loop), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) + theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 0,
),
axis.text.y = element_text(
size = fontSizeS,
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.9))) +
annotate(
\text\, x = 1, y = quantile(data$sumScore, 0.5),
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
) + guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
)
)
fileName <- paste0(\ChIP_peak_avgPeakScore_\, name)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# Sum peak score
####
track <- import(here(refDir, \33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \H3K4me3\)
plotSumScoresBinary(track, peak, \H3K4me3\, anchor.upno, anchor.down)
track <- import(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
#plotSumScores(track, peak, \H3K27ac\)
plotSumScoresBinary(track, peak, \H3K27ac\, anchor.upno, anchor.down)
track <- import(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.bed\))
#plotSumScores(track, peak, \H3K14ac\)
plotSumScoresBinary(track, peak, \H3K14ac\, anchor.upno, anchor.down)
track <- import(here(refDir, \33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \CTCF\)
plotSumScoresBinary(track, peak, \CTCF\, anchor.upno, anchor.down)
track <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \RAD21\)
plotSumScoresBinary(track, peak, \RAD21\, anchor.upno, anchor.down)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### P-N
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\\))\nanchor.up <- (extractAnchor(loop.up))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\\))\nanchor.no <- (extractAnchor(loop.no))\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- (extractAnchor(loop.down))\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, sum, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScores <- function(track, peak, name){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor.up)\n  b <- getSumScores(peakTrack, anchor.no)\n  c <- getSumScores(peakTrack, anchor.down)\n  a.tb <- tibble(loop = \\UP\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\NO\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\DOWN\\,\n                 sumScore = c)\n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  ggplot(data, aes(x = loop, y = sumScore)) + geom_boxplot(outlier.shape = NA) + theme_classic() + ggtitle(paste0(name, \\ sum peak\\)) +\n    coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScores(track, peak, \\H3K4me3\\)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\nplotSumScores(track, peak, \\H3K27ac\\)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\nplotSumScores(track, peak, \\H3K14ac\\)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScores(track, peak, \\CTCF\\)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak.temp <- fread(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\)) %>%\n  dplyr::mutate(V2 = V2 - 1000,\n                V3 = V3 + 1000)\ncolnames(peak.temp) <- c(\\chr\\, \\start\\, \\end\\)\npeak <- makeGRangesFromDataFrame(peak.temp)\nplotSumScores(track, peak, \\YY1\\)\n\n\n### For those with only summit\ntrack <- import(here(refDir, \\GSM5571895_ESC_YY1_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM5571895_ESC_YY1_summit.bed\\))\nplotSumScores(track, peak, \\RAD21\\)\n\n### FOR THOSE WITHOUT PEAKS\ntrack <- import(here(refDir, \\GSM2082708_ESC.H3K27me3.1_mm10Lifted.black.bw\\), format = \\BigWig\\)\na <- getSumScores(track, anchor.up)\nb <- getSumScores(track, anchor.no)\nc <- getSumScores(track, anchor.down)\na.tb <- tibble(loop = \\UP\\,\n               sumScore = a)\nb.tb <- tibble(loop = \\NO\\,\n               sumScore = b)\nc.tb <- tibble(loop = \\DOWN\\,\n               sumScore = c)\ndata <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\nggplot(data, aes(x = loop, y = sumScore)) + geom_boxplot(outlier.shape = NA) + theme_classic() + ggtitle(paste0(\\H3K27me3 sum no peak\\)) +\n  coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
getSumScores <- function(track, anchor) {
# Find overlaps between all anchors and track regions at once
overlaps <- findOverlaps(anchor, track)
# Extract the scores and corresponding anchor indices
anchor_indices <- queryHits(overlaps)
track_scores <- score(track)[subjectHits(overlaps)]
# Use tapply to calculate the median scores for each anchor
median_scores <- tapply(track_scores, anchor_indices, sum, na.rm = TRUE)
# Initialize a numeric vector to store the median scores for each anchor
all_median_scores <- rep(NA, length(anchor))
# Populate the median scores for the anchors that have overlaps
all_median_scores[as.numeric(names(median_scores))] <- median_scores
return(all_median_scores)
}
plotSumScores <- function(track, peak, name){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
a <- getSumScores(peakTrack, anchor.up)
b <- getSumScores(peakTrack, anchor.no)
c <- getSumScores(peakTrack, anchor.down)
a.tb <- tibble(loop = \UP\,
sumScore = a)
b.tb <- tibble(loop = \NO\,
sumScore = b)
c.tb <- tibble(loop = \DOWN\,
sumScore = c)
data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()
ggplot(data, aes(x = loop, y = sumScore)) + geom_boxplot(outlier.shape = NA) + theme_classic() + ggtitle(paste0(name, \ sum peak\)) +
coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))
}
# Sum peak score
####
track <- import(here(refDir, \33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
plotSumScores(track, peak, \H3K4me3\)
track <- import(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
plotSumScores(track, peak, \H3K27ac\)
track <- import(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.bed\))
plotSumScores(track, peak, \H3K14ac\)
track <- import(here(refDir, \33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
plotSumScores(track, peak, \CTCF\)
track <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak.temp <- fread(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\)) %>%
dplyr::mutate(V2 = V2 - 1000,
V3 = V3 + 1000)
colnames(peak.temp) <- c(\chr\, \start\, \end\)
peak <- makeGRangesFromDataFrame(peak.temp)
plotSumScores(track, peak, \YY1\)
### For those with only summit
track <- import(here(refDir, \GSM5571895_ESC_YY1_1.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM5571895_ESC_YY1_summit.bed\))
plotSumScores(track, peak, \RAD21\)
### FOR THOSE WITHOUT PEAKS
track <- import(here(refDir, \GSM2082708_ESC.H3K27me3.1_mm10Lifted.black.bw\), format = \BigWig\)
a <- getSumScores(track, anchor.up)
b <- getSumScores(track, anchor.no)
c <- getSumScores(track, anchor.down)
a.tb <- tibble(loop = \UP\,
sumScore = a)
b.tb <- tibble(loop = \NO\,
sumScore = b)
c.tb <- tibble(loop = \DOWN\,
sumScore = c)
data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()
ggplot(data, aes(x = loop, y = sumScore)) + geom_boxplot(outlier.shape = NA) + theme_classic() + ggtitle(paste0(\H3K27me3 sum no peak\)) +
coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\\))\nanchor.up <- (extractAnchor(loop.up))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\\))\nanchor.no <- (extractAnchor(loop.no))\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- (extractAnchor(loop.down))\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, sum, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScores <- function(track, peak, name){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor.up)\n  b <- getSumScores(peakTrack, anchor.no)\n  c <- getSumScores(peakTrack, anchor.down)\n  a.tb <- tibble(loop = \\UP\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\NO\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\DOWN\\,\n                 sumScore = c)\n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  ggplot(data, aes(x = loop, y = sumScore)) + geom_boxplot(outlier.shape = NA) + theme_classic() + ggtitle(paste0(name, \\ sum peak\\)) +\n    coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScores(track, peak, \\H3K4me3\\)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\nplotSumScores(track, peak, \\H3K27ac\\)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\nplotSumScores(track, peak, \\H3K14ac\\)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScores(track, peak, \\CTCF\\)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak.temp <- fread(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\)) %>%\n  dplyr::mutate(V2 = V2 - 1000,\n                V3 = V3 + 1000)\ncolnames(peak.temp) <- c(\\chr\\, \\start\\, \\end\\)\npeak <- makeGRangesFromDataFrame(peak.temp)\nplotSumScores(track, peak, \\YY1\\)\n\n\n### For those with only summit\ntrack <- import(here(refDir, \\GSM5571895_ESC_YY1_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM5571895_ESC_YY1_summit.bed\\))\nplotSumScores(track, peak, \\RAD21\\)\n\n### FOR THOSE WITHOUT PEAKS\ntrack <- import(here(refDir, \\GSM2082708_ESC.H3K27me3.1_mm10Lifted.black.bw\\), format = \\BigWig\\)\na <- getSumScores(track, anchor.up)\nb <- getSumScores(track, anchor.no)\nc <- getSumScores(track, anchor.down)\na.tb <- tibble(loop = \\UP\\,\n               sumScore = a)\nb.tb <- tibble(loop = \\NO\\,\n               sumScore = b)\nc.tb <- tibble(loop = \\DOWN\\,\n               sumScore = c)\ndata <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\nggplot(data, aes(x = loop, y = sumScore)) + geom_boxplot(outlier.shape = NA) + theme_classic() + ggtitle(paste0(\\H3K27me3 sum no peak\\)) +\n  coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
getSumScores <- function(track, anchor) {
# Find overlaps between all anchors and track regions at once
overlaps <- findOverlaps(anchor, track)
# Extract the scores and corresponding anchor indices
anchor_indices <- queryHits(overlaps)
track_scores <- score(track)[subjectHits(overlaps)]
# Use tapply to calculate the median scores for each anchor
median_scores <- tapply(track_scores, anchor_indices, sum, na.rm = TRUE)
# Initialize a numeric vector to store the median scores for each anchor
all_median_scores <- rep(NA, length(anchor))
# Populate the median scores for the anchors that have overlaps
all_median_scores[as.numeric(names(median_scores))] <- median_scores
return(all_median_scores)
}
plotSumScores <- function(track, peak, name){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
a <- getSumScores(peakTrack, anchor.up)
b <- getSumScores(peakTrack, anchor.no)
c <- getSumScores(peakTrack, anchor.down)
a.tb <- tibble(loop = \UP\,
sumScore = a)
b.tb <- tibble(loop = \NO\,
sumScore = b)
c.tb <- tibble(loop = \DOWN\,
sumScore = c)
data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()
ggplot(data, aes(x = loop, y = sumScore)) + geom_boxplot(outlier.shape = NA) + theme_classic() + ggtitle(paste0(name, \ sum peak\)) +
coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))
}
# Sum peak score
####
track <- import(here(refDir, \33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
plotSumScores(track, peak, \H3K4me3\)
track <- import(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
plotSumScores(track, peak, \H3K27ac\)
track <- import(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.bed\))
plotSumScores(track, peak, \H3K14ac\)
track <- import(here(refDir, \33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
plotSumScores(track, peak, \CTCF\)
track <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak.temp <- fread(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\)) %>%
dplyr::mutate(V2 = V2 - 1000,
V3 = V3 + 1000)
colnames(peak.temp) <- c(\chr\, \start\, \end\)
peak <- makeGRangesFromDataFrame(peak.temp)
plotSumScores(track, peak, \YY1\)
### For those with only summit
track <- import(here(refDir, \GSM5571895_ESC_YY1_1.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM5571895_ESC_YY1_summit.bed\))
plotSumScores(track, peak, \RAD21\)
### FOR THOSE WITHOUT PEAKS
track <- import(here(refDir, \GSM2082708_ESC.H3K27me3.1_mm10Lifted.black.bw\), format = \BigWig\)
a <- getSumScores(track, anchor.up)
b <- getSumScores(track, anchor.no)
c <- getSumScores(track, anchor.down)
a.tb <- tibble(loop = \UP\,
sumScore = a)
b.tb <- tibble(loop = \NO\,
sumScore = b)
c.tb <- tibble(loop = \DOWN\,
sumScore = c)
data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()
ggplot(data, aes(x = loop, y = sumScore)) + geom_boxplot(outlier.shape = NA) + theme_classic() + ggtitle(paste0(\H3K27me3 sum no peak\)) +
coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))
```
```
<!-- rnb-source-end -->
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<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.26] Checking the ChIP intensity at anchors
This doesn't have to be restricted to P-N loops. Let's check all loops first.
Also instead of density (mean), median could be better choice. Sum is not appropriate here since
the size of anchors are not same.
#### Limited to peaks to reduce noise
##### dTAG
<!-- rnb-text-end -->
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name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
peak.H3K4me3 <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
peak.CTCF <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
peak.temp <- fread(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\)) %>%
dplyr::mutate(V2 = V2 - 1000,
V3 = V3 + 1000)
colnames(peak.temp) <- c(\chr\, \start\, \end\)
peak.RAD21 <- makeGRangesFromDataFrame(peak.temp)
calculatePeakDensity <- function(peak, note){
overlap_counts <- countOverlaps(anchor.up, peak)
anchor_widths <- (width(anchor.up)-1)/1000
density <- overlap_counts / anchor_widths
data1 <- tibble(type = \UP\,
densityPerKb = density)
overlap_counts <- countOverlaps(anchor.no, peak)
anchor_widths <- (width(anchor.no)-1)/1000
density <- overlap_counts / anchor_widths
mcols(anchor.no)$density <- density
data2 <- tibble(type = \NO\,
densityPerKb = density)
overlap_counts <- countOverlaps(anchor.down, peak)
anchor_widths <- (width(anchor.down)-1)/1000
density <- overlap_counts / anchor_widths
mcols(anchor.down)$density <- density
data3 <- tibble(type = \DOWN\,
densityPerKb = density)
data <- bind_rows(data1, data2, data3)
ggplot(data, aes(x = type, y = densityPerKb)) + geom_violin() + geom_boxplot(outlier.shape = NA, width = 0.1 ) + theme_bw() + ggtitle(note) +
coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))
}
calculatePeakDensity(peak.H3K4me3, \H3K4me3\)
calculatePeakDensity(peak.H3K27ac, \H3K27ac\)
calculatePeakDensity(peak.CTCF, \CTCF\)
calculatePeakDensity(peak.RAD21, \RAD21\)
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-->
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```r
```r
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
peak.H3K4me3 <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
peak.CTCF <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
peak.temp <- fread(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\)) %>%
dplyr::mutate(V2 = V2 - 1000,
V3 = V3 + 1000)
colnames(peak.temp) <- c(\chr\, \start\, \end\)
peak.RAD21 <- makeGRangesFromDataFrame(peak.temp)
calculatePeakDensity <- function(peak, note){
overlap_counts <- countOverlaps(anchor.up, peak)
anchor_widths <- (width(anchor.up)-1)/1000
density <- overlap_counts / anchor_widths
data1 <- tibble(type = \UP\,
densityPerKb = density)
overlap_counts <- countOverlaps(anchor.no, peak)
anchor_widths <- (width(anchor.no)-1)/1000
density <- overlap_counts / anchor_widths
mcols(anchor.no)$density <- density
data2 <- tibble(type = \NO\,
densityPerKb = density)
overlap_counts <- countOverlaps(anchor.down, peak)
anchor_widths <- (width(anchor.down)-1)/1000
density <- overlap_counts / anchor_widths
mcols(anchor.down)$density <- density
data3 <- tibble(type = \DOWN\,
densityPerKb = density)
data <- bind_rows(data1, data2, data3)
ggplot(data, aes(x = type, y = densityPerKb)) + geom_violin() + geom_boxplot(outlier.shape = NA, width = 0.1 ) + theme_bw() + ggtitle(note) +
coord_cartesian(ylim = c(0, quantile(data$sumScore, 0.9)))
}
calculatePeakDensity(peak.H3K4me3, \H3K4me3\)
calculatePeakDensity(peak.H3K27ac, \H3K27ac\)
calculatePeakDensity(peak.CTCF, \CTCF\)
calculatePeakDensity(peak.RAD21, \RAD21\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### A485
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVibUZ0WlNBOExTQmNJbU5vY205dGIxOWpiMjV6WDJGdWJtOUlhV1Z5WVhKamFIbGNJbHh1Ykc5dmNDQThMU0JtY21WaFpDaG9aWEpsS0dOdmJuTmxibk4xYzBScGNpd2dYQ0pqYUhKdmJXOWZZMjl1YzE5aGJtNXZTR2xsY21GeVkyaDVYM0JsTFhCbExtSmxaSEJsWENJcEtWeHVZVzVqYUc5eUlEd3RJR1Y0ZEhKaFkzUkJibU5vYjNJb2JHOXZjQ2xjYmx4dUl5TWpJRmRvZVhSbFhHNXVJRHd0SUd4bGJtZDBhQ2gxYm1seGRXVW9jM1ZpYW1WamRFaHBkSE1vWm1sdVpFOTJaWEpzWVhCektHRnVZMmh2Y2l3Z2NHVmhheTVYYUhsMFpTNVRSU2twS1NsY2JuUnZkR0ZzSUR3dElHeGxibWQwYUNod1pXRnJMbGRvZVhSbExsTkZLVnh1WTI5MWJuUnpJRHd0SUdNb2Jpd2dkRzkwWVd3Z0xTQnVLVnh1YkdGaVpXeHpJRHd0SUhCaGMzUmxLR01vWENKamIzWmxjbVZrWENJc0lGd2lUazlVSUdOdmRtVnlaV1JjSWlrc0lHTnZkVzUwY3lsY2JuQnBaU2hqYjNWdWRITXNJR3hoWW1Wc2N5QTlJR3hoWW1Wc2N5d2dJRzFoYVc0Z1BTQndZWE4wWlRBb1hDSlhhSGwwWlNCVFJTQmpiM1psY21GblpTQmNYRzVjSWl3Z2RHOTBZV3dwTENCamIyd2dQU0JqS0Z3aVozSmxlVndpTENCY0luZG9hWFJsWENJcEtWeHVYRzVtYVd4bFRtRnRaU0E4TFNCb1pYSmxLR1pwWjBScGNpd2dYQ0p3YVdWZlUwVmZWMmg1ZEdWY0lpbGNibmRwWkhSb0lEd3RJSEJoYm1Wc1UybDZaU2cwS1NwdGJWUnZTVzVqYUZ4dWFHVnBaMmgwSUR3dElIQmhibVZzVTJsNlpTZzBLU3B0YlZSdlNXNWphRnh1SXlCemRtZHNhWFJsS0hCaGMzUmxNQ2htYVd4bFRtRnRaU3dnWENJdWMzWm5YQ0lwTENCM2FXUjBhQ0E5SUhkcFpIUm9MQ0JvWldsbmFIUWdQV2hsYVdkb2RDbGNiaU1nY0dsbEtHTnZkVzUwY3l3Z2JHRmlaV3h6SUQwZ2JHRmlaV3h6TENBZ2JXRnBiaUE5SUhCaGMzUmxNQ2hjSWxkb2VYUmxJRk5GSUdOdmRtVnlZV2RsSUZ4Y2Jsd2lMQ0IwYjNSaGJDa3NJR052YkNBOUlHTW9YQ0puY21WNVhDSXNJRndpZDJocGRHVmNJaWtzWEc0aklDQWdJQ0JqWlhnZ1BTQXhMQ0JqWlhndWJXRnBiaUE5SURFcFhHNGpJR1JsZGk1dlptWW9LVnh1WEc1Y2JseHVJeU1qSUVSNWJHRnVYRzV1SUR3dElHeGxibWQwYUNoMWJtbHhkV1VvYzNWaWFtVmpkRWhwZEhNb1ptbHVaRTkyWlhKc1lYQnpLR0Z1WTJodmNpd2djR1ZoYXk1RWVXeGhiaTVUUlNrcEtTbGNiblJ2ZEdGc0lEd3RJR3hsYm1kMGFDaHdaV0ZyTGtSNWJHRnVMbE5GS1Z4dVkyOTFiblJ6SUR3dElHTW9iaXdnZEc5MFlXd2dMU0J1S1Z4dWJHRmlaV3h6SUR3dElIQmhjM1JsS0dNb1hDSmpiM1psY21Wa1hDSXNJRndpVGs5VUlHTnZkbVZ5WldSY0lpa3NJR052ZFc1MGN5bGNibkJwWlNoamIzVnVkSE1zSUd4aFltVnNjeUE5SUd4aFltVnNjeXdnYldGcGJpQTlJSEJoYzNSbE1DaGNJa1I1YkdGdUlGTkZJR052ZG1WeVlXZGxJRnhjYmx3aUxDQjBiM1JoYkNrc0lHTnZiQ0E5SUdNb1hDSm5jbVY1WENJc0lGd2lkMmhwZEdWY0lpa3BYRzVjYm1acGJHVk9ZVzFsSUR3dElHaGxjbVVvWm1sblJHbHlMQ0JjSW5CcFpWOVRSVjlOZFhKd2FIbGNJaWxjYm5kcFpIUm9JRHd0SUhCaGJtVnNVMmw2WlNnMEtTcHRiVlJ2U1c1amFGeHVhR1ZwWjJoMElEd3RJSEJoYm1Wc1UybDZaU2cwS1NwdGJWUnZTVzVqYUZ4dUl5QnpkbWRzYVhSbEtIQmhjM1JsTUNobWFXeGxUbUZ0WlN3Z1hDSXVjM1puWENJcExDQjNhV1IwYUNBOUlIZHBaSFJvTENCb1pXbG5hSFFnUFdobGFXZG9kQ2xjYmlNZ2NHbGxLR052ZFc1MGN5d2diR0ZpWld4eklEMGdiR0ZpWld4ekxDQnRZV2x1SUQwZ2NHRnpkR1V3S0Z3aVRYVnljR2g1SUZORklHTnZkbVZ5WVdkbElGeGNibHdpTENCMGIzUmhiQ2tzSUdOdmJDQTlJR01vWENKbmNtVjVYQ0lzSUZ3aWQyaHBkR1ZjSWlrcFhHNGpJRnh1SXlCa1pYWXViMlptS0NsY2JseHVZR0JnSW4wPSAtLT5cblxuYGBgclxubmFtZSA8LSBcXGNocm9tb19jb25zX2Fubm9IaWVyYXJjaHlcXFxubG9vcCA8LSBmcmVhZChoZXJlKGNvbnNlbnN1c0RpciwgXFxjaHJvbW9fY29uc19hbm5vSGllcmFyY2h5X3BlLXBlLmJlZHBlXFwpKVxuYW5jaG9yIDwtIGV4dHJhY3RBbmNob3IobG9vcClcblxuIyMjIFdoeXRlXG5uIDwtIGxlbmd0aCh1bmlxdWUoc3ViamVjdEhpdHMoZmluZE92ZXJsYXBzKGFuY2hvciwgcGVhay5XaHl0ZS5TRSkpKSlcbnRvdGFsIDwtIGxlbmd0aChwZWFrLldoeXRlLlNFKVxuY291bnRzIDwtIGMobiwgdG90YWwgLSBuKVxubGFiZWxzIDwtIHBhc3RlKGMoXFxjb3ZlcmVkXFwsIFxcTk9UIGNvdmVyZWRcXCksIGNvdW50cylcbnBpZShjb3VudHMsIGxhYmVscyA9IGxhYmVscywgIG1haW4gPSBwYXN0ZTAoXFxXaHl0ZSBTRSBjb3ZlcmFnZSBcXG5cXCwgdG90YWwpLCBjb2wgPSBjKFxcZ3JleVxcLCBcXHdoaXRlXFwpKVxuXG5maWxlTmFtZSA8LSBoZXJlKGZpZ0RpciwgXFxwaWVfU0VfV2h5dGVcXClcbndpZHRoIDwtIHBhbmVsU2l6ZSg0KSptbVRvSW5jaFxuaGVpZ2h0IDwtIHBhbmVsU2l6ZSg0KSptbVRvSW5jaFxuIyBzdmdsaXRlKHBhc3RlMChmaWxlTmFtZSwgXFwuc3ZnXFwpLCB3aWR0aCA9IHdpZHRoLCBoZWlnaHQgPWhlaWdodClcbiMgcGllKGNvdW50cywgbGFiZWxzID0gbGFiZWxzLCAgbWFpbiA9IHBhc3RlMChcXFdoeXRlIFNFIGNvdmVyYWdlIFxcblxcLCB0b3RhbCksIGNvbCA9IGMoXFxncmV5XFwsIFxcd2hpdGVcXCksXG4jICAgICBjZXggPSAxLCBjZXgubWFpbiA9IDEpXG4jIGRldi5vZmYoKVxuXG5cblxuIyMjIER5bGFuXG5uIDwtIGxlbmd0aCh1bmlxdWUoc3ViamVjdEhpdHMoZmluZE92ZXJsYXBzKGFuY2hvciwgcGVhay5EeWxhbi5TRSkpKSlcbnRvdGFsIDwtIGxlbmd0aChwZWFrLkR5bGFuLlNFKVxuY291bnRzIDwtIGMobiwgdG90YWwgLSBuKVxubGFiZWxzIDwtIHBhc3RlKGMoXFxjb3ZlcmVkXFwsIFxcTk9UIGNvdmVyZWRcXCksIGNvdW50cylcbnBpZShjb3VudHMsIGxhYmVscyA9IGxhYmVscywgbWFpbiA9IHBhc3RlMChcXER5bGFuIFNFIGNvdmVyYWdlIFxcblxcLCB0b3RhbCksIGNvbCA9IGMoXFxncmV5XFwsIFxcd2hpdGVcXCkpXG5cbmZpbGVOYW1lIDwtIGhlcmUoZmlnRGlyLCBcXHBpZV9TRV9NdXJwaHlcXClcbndpZHRoIDwtIHBhbmVsU2l6ZSg0KSptbVRvSW5jaFxuaGVpZ2h0IDwtIHBhbmVsU2l6ZSg0KSptbVRvSW5jaFxuIyBzdmdsaXRlKHBhc3RlMChmaWxlTmFtZSwgXFwuc3ZnXFwpLCB3aWR0aCA9IHdpZHRoLCBoZWlnaHQgPWhlaWdodClcbiMgcGllKGNvdW50cywgbGFiZWxzID0gbGFiZWxzLCBtYWluID0gcGFzdGUwKFxcTXVycGh5IFNFIGNvdmVyYWdlIFxcblxcLCB0b3RhbCksIGNvbCA9IGMoXFxncmV5XFwsIFxcd2hpdGVcXCkpXG4jIFxuIyBkZXYub2ZmKClcblxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
name <- \chromo_cons_annoHierarchy\
loop <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
anchor <- extractAnchor(loop)
### Whyte
n <- length(unique(subjectHits(findOverlaps(anchor, peak.Whyte.SE))))
total <- length(peak.Whyte.SE)
counts <- c(n, total - n)
labels <- paste(c(\covered\, \NOT covered\), counts)
pie(counts, labels = labels, main = paste0(\Whyte SE coverage \n\, total), col = c(\grey\, \white\))
fileName <- here(figDir, \pie_SE_Whyte\)
width <- panelSize(4)*mmToInch
height <- panelSize(4)*mmToInch
# svglite(paste0(fileName, \.svg\), width = width, height =height)
# pie(counts, labels = labels, main = paste0(\Whyte SE coverage \n\, total), col = c(\grey\, \white\),
# cex = 1, cex.main = 1)
# dev.off()
### Dylan
n <- length(unique(subjectHits(findOverlaps(anchor, peak.Dylan.SE))))
total <- length(peak.Dylan.SE)
counts <- c(n, total - n)
labels <- paste(c(\covered\, \NOT covered\), counts)
pie(counts, labels = labels, main = paste0(\Dylan SE coverage \n\, total), col = c(\grey\, \white\))
fileName <- here(figDir, \pie_SE_Murphy\)
width <- panelSize(4)*mmToInch
height <- panelSize(4)*mmToInch
# svglite(paste0(fileName, \.svg\), width = width, height =height)
# pie(counts, labels = labels, main = paste0(\Murphy SE coverage \n\, total), col = c(\grey\, \white\))
#
# dev.off()
<!-- rnb-output-end -->
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```r
```r
name <- \chromo_cons_annoHierarchy\
loop <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
anchor <- extractAnchor(loop)
### Whyte
n <- length(unique(subjectHits(findOverlaps(anchor, peak.Whyte.SE))))
total <- length(peak.Whyte.SE)
counts <- c(n, total - n)
labels <- paste(c(\covered\, \NOT covered\), counts)
pie(counts, labels = labels, main = paste0(\Whyte SE coverage \n\, total), col = c(\grey\, \white\))
fileName <- here(figDir, \pie_SE_Whyte\)
width <- panelSize(4)*mmToInch
height <- panelSize(4)*mmToInch
# svglite(paste0(fileName, \.svg\), width = width, height =height)
# pie(counts, labels = labels, main = paste0(\Whyte SE coverage \n\, total), col = c(\grey\, \white\),
# cex = 1, cex.main = 1)
# dev.off()
### Dylan
n <- length(unique(subjectHits(findOverlaps(anchor, peak.Dylan.SE))))
total <- length(peak.Dylan.SE)
counts <- c(n, total - n)
labels <- paste(c(\covered\, \NOT covered\), counts)
pie(counts, labels = labels, main = paste0(\Dylan SE coverage \n\, total), col = c(\grey\, \white\))
fileName <- here(figDir, \pie_SE_Murphy\)
width <- panelSize(4)*mmToInch
height <- panelSize(4)*mmToInch
# svglite(paste0(fileName, \.svg\), width = width, height =height)
# pie(counts, labels = labels, main = paste0(\Murphy SE coverage \n\, total), col = c(\grey\, \white\))
#
# dev.off()
```
```
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#### Peak density instead of intensity
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ZSwgXCIuc3ZnXCIpLCAgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuXG5gYGAifQ== -->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\nloop <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\\Overlapping\\, \\Not_Overlapping\\)\n  rownames(contingency_table) <- c(\\All loops\\, \\Subset loops\\)\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\n\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nloop.upno <- bind_rows(loop.up, loop.no)\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\n\n### Dylan\n# Seeding\ntemp <- getSEOverlapFisher(loop, loop.upno, peak.Dylan.SE)\nresult.tb <- tibble(loopType = \\UP/NO\\,\n                    target = \\Dylan SE\\,\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n\n# temp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)\n# result.tb <- tibble(loopType = \\UP\\,\n#                     target = \\Dylan SE\\,\n#                     pvalue = temp$p.value,\n#                     oddsRatio = temp$estimate)\n# Add row\n# temp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)\n# result.tb <- result.tb %>% \n#   add_row(loopType = \\NO\\,\n#           target = \\Dylan SE\\,\n#           pvalue = temp$p.value,\n#           oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Dylan SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n### Whyte\n# Seeding\n# temp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)\n# result.tb <- result.tb %>% \n#   add_row(loopType = \\UP\\,\n#                     target = \\Whyte SE\\,\n#                     pvalue = temp$p.value,\n#                     oddsRatio = temp$estimate)\n\n# Add row\ntemp <- getSEOverlapFisher(loop, loop.upno, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\UP/NO\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\n# Visualization\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\#4852A0\\, \\white\\, \\#CB333A\\))\n\np <- Heatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = fontSizeS, fontfamily = fontType))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        row_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),\n  column_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),\n        heatmap_legend_param = list(\n          at = c(0, 1, 2),\n          labels = c(\\0\\, \\1\\, \\2\\),\n          title_gp = gpar(fontfamily = fontType, fontsize = fontSizeS),\n          labels_gp = gpar(fontfamily = fontType, fontsize = fontSizeS)\n        )\n)\n\n# fileName <- here(figDir, \\heatmap_SE_enrichment\\)\n# width <- panelSize(1.5)*mmToInch\n# height <- panelSize(0.7)*mmToInch\n# svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n# print(p)\n# dev.off()\n# png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n# print(p)\n# dev.off()\n\n#######################\n\ndata$loopType <- factor(data$loopType, levels = c(\\UP/NO\\, \\DOWN\\))\ndata$target <- factor(data$target, levels = c(\\Whyte SE\\, \\Dylan SE\\))\n\np <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) +\n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\heatmap_SE_enrichment_dotplot\\)\nwidth <- panelSize(1.8)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
loop <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
getOverlapLoopNum <- function(loop, peak){
anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))
anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))
a <- queryHits(findOverlaps(anchor1, peak))
b <- queryHits(findOverlaps(anchor2, peak))
return(length(unique(c(a, b))))
}
getSEOverlapFisher <- function(allLoop, subsetLoop, peak){
all.overlap <- getOverlapLoopNum(allLoop, peak)
all.notOverlap <- nrow(allLoop) - all.overlap
subset.overlap <- getOverlapLoopNum(subsetLoop, peak)
subset.notOverlap <- nrow(subsetLoop) - subset.overlap
contingency_table <- matrix(c(subset.overlap, subset.notOverlap,
all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)
colnames(contingency_table) <- c(\Overlapping\, \Not_Overlapping\)
rownames(contingency_table) <- c(\All loops\, \Subset loops\)
# Perform Fisher's Exact Test
fisher_test_result <- fisher.test(contingency_table)
return(fisher_test_result)
}
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
loop.upno <- bind_rows(loop.up, loop.no)
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
### Dylan
# Seeding
temp <- getSEOverlapFisher(loop, loop.upno, peak.Dylan.SE)
result.tb <- tibble(loopType = \UP/NO\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# temp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)
# result.tb <- tibble(loopType = \UP\,
# target = \Dylan SE\,
# pvalue = temp$p.value,
# oddsRatio = temp$estimate)
# Add row
# temp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)
# result.tb <- result.tb %>%
# add_row(loopType = \NO\,
# target = \Dylan SE\,
# pvalue = temp$p.value,
# oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
### Whyte
# Seeding
# temp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)
# result.tb <- result.tb %>%
# add_row(loopType = \UP\,
# target = \Whyte SE\,
# pvalue = temp$p.value,
# oddsRatio = temp$estimate)
# Add row
temp <- getSEOverlapFisher(loop, loop.upno, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \UP/NO\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# Visualization
library(circlize)
data <- result.tb
heatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%
pivot_wider(names_from = loopType, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%
pivot_wider(names_from = loopType, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 2),
c(\#4852A0\, \white\, \#CB333A\))
p <- Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = fontSizeS, fontfamily = fontType))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
row_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),
column_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),
heatmap_legend_param = list(
at = c(0, 1, 2),
labels = c(\0\, \1\, \2\),
title_gp = gpar(fontfamily = fontType, fontsize = fontSizeS),
labels_gp = gpar(fontfamily = fontType, fontsize = fontSizeS)
)
)
# fileName <- here(figDir, \heatmap_SE_enrichment\)
# width <- panelSize(1.5)*mmToInch
# height <- panelSize(0.7)*mmToInch
# svglite(paste0(fileName, \.svg\), width = width, height =height)
# print(p)
# dev.off()
# png(paste0(fileName, \.png\), width = width, height =height, res = 600, unit = \in\)
# print(p)
# dev.off()
#######################
data$loopType <- factor(data$loopType, levels = c(\UP/NO\, \DOWN\))
data$target <- factor(data$target, levels = c(\Whyte SE\, \Dylan SE\))
p <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
scale_fill_gradientn(colors = c(\#4852A0\, \white\, \#CB333A\), # Define gradient colors
values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5),
#low = \white\, high = \#CB333A\,
# limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \heatmap_SE_enrichment_dotplot\)
width <- panelSize(1.8)*mmToInch
height <- panelSize(1.1)*mmToInch
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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aWxlTmFtZSwgXFwuc3ZnXFwpLCAgaGVpZ2h0ID0gaGVpZ2h0LCB3aWR0aCA9IHdpZHRoKVxucHJpbnQocClcbmRldi5vZmYoKVxuXG5gYGBcbmBgYCJ9 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\nloop <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\\Overlapping\\, \\Not_Overlapping\\)\n  rownames(contingency_table) <- c(\\All loops\\, \\Subset loops\\)\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\n\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nloop.upno <- bind_rows(loop.up, loop.no)\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\n\n### Dylan\n# Seeding\ntemp <- getSEOverlapFisher(loop, loop.upno, peak.Dylan.SE)\nresult.tb <- tibble(loopType = \\UP/NO\\,\n                    target = \\Dylan SE\\,\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n\n# temp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)\n# result.tb <- tibble(loopType = \\UP\\,\n#                     target = \\Dylan SE\\,\n#                     pvalue = temp$p.value,\n#                     oddsRatio = temp$estimate)\n# Add row\n# temp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)\n# result.tb <- result.tb %>% \n#   add_row(loopType = \\NO\\,\n#           target = \\Dylan SE\\,\n#           pvalue = temp$p.value,\n#           oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Dylan SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n### Whyte\n# Seeding\n# temp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)\n# result.tb <- result.tb %>% \n#   add_row(loopType = \\UP\\,\n#                     target = \\Whyte SE\\,\n#                     pvalue = temp$p.value,\n#                     oddsRatio = temp$estimate)\n\n# Add row\ntemp <- getSEOverlapFisher(loop, loop.upno, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\UP/NO\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\n# Visualization\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\#4852A0\\, \\white\\, \\#CB333A\\))\n\np <- Heatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = fontSizeS, fontfamily = fontType))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        row_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),\n  column_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),\n        heatmap_legend_param = list(\n          at = c(0, 1, 2),\n          labels = c(\\0\\, \\1\\, \\2\\),\n          title_gp = gpar(fontfamily = fontType, fontsize = fontSizeS),\n          labels_gp = gpar(fontfamily = fontType, fontsize = fontSizeS)\n        )\n)\n\n# fileName <- here(figDir, \\heatmap_SE_enrichment\\)\n# width <- panelSize(1.5)*mmToInch\n# height <- panelSize(0.7)*mmToInch\n# svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n# print(p)\n# dev.off()\n# png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n# print(p)\n# dev.off()\n\n#######################\n\ndata$loopType <- factor(data$loopType, levels = c(\\UP/NO\\, \\DOWN\\))\ndata$target <- factor(data$target, levels = c(\\Whyte SE\\, \\Dylan SE\\))\n\np <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) +\n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\heatmap_SE_enrichment_dotplot\\)\nwidth <- panelSize(1.8)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\nloop <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\\Overlapping\\, \\Not_Overlapping\\)\n  rownames(contingency_table) <- c(\\All loops\\, \\Subset loops\\)\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\n\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nloop.upno <- bind_rows(loop.up, loop.no)\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\n\n### Dylan\n# Seeding\ntemp <- getSEOverlapFisher(loop, loop.upno, peak.Dylan.SE)\nresult.tb <- tibble(loopType = \\UP/NO\\,\n                    target = \\Dylan SE\\,\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n\n# temp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)\n# result.tb <- tibble(loopType = \\UP\\,\n#                     target = \\Dylan SE\\,\n#                     pvalue = temp$p.value,\n#                     oddsRatio = temp$estimate)\n# Add row\n# temp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)\n# result.tb <- result.tb %>% \n#   add_row(loopType = \\NO\\,\n#           target = \\Dylan SE\\,\n#           pvalue = temp$p.value,\n#           oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Dylan SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n### Whyte\n# Seeding\n# temp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)\n# result.tb <- result.tb %>% \n#   add_row(loopType = \\UP\\,\n#                     target = \\Whyte SE\\,\n#                     pvalue = temp$p.value,\n#                     oddsRatio = temp$estimate)\n\n# Add row\ntemp <- getSEOverlapFisher(loop, loop.upno, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\UP/NO\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\n# Visualization\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 2), \n                      c(\\#4852A0\\, \\white\\, \\#CB333A\\))\n\np <- Heatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = fontSizeS, fontfamily = fontType))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        row_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),\n  column_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),\n        heatmap_legend_param = list(\n          at = c(0, 1, 2),\n          labels = c(\\0\\, \\1\\, \\2\\),\n          title_gp = gpar(fontfamily = fontType, fontsize = fontSizeS),\n          labels_gp = gpar(fontfamily = fontType, fontsize = fontSizeS)\n        )\n)\n\n# fileName <- here(figDir, \\heatmap_SE_enrichment\\)\n# width <- panelSize(1.5)*mmToInch\n# height <- panelSize(0.7)*mmToInch\n# svglite(paste0(fileName, \\.svg\\), width = width, height =height)\n# print(p)\n# dev.off()\n# png(paste0(fileName, \\.png\\), width = width, height =height, res = 600, unit = \\in\\)\n# print(p)\n# dev.off()\n\n#######################\n\ndata$loopType <- factor(data$loopType, levels = c(\\UP/NO\\, \\DOWN\\))\ndata$target <- factor(data$target, levels = c(\\Whyte SE\\, \\Dylan SE\\))\n\np <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) +\n  labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\nfileName <- here(figDir, \\heatmap_SE_enrichment_dotplot\\)\nwidth <- panelSize(1.8)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# print(p)\n# dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n\n```\n```"} -->
```r
```r
name <- \chromo_cons_annoHierarchy\
loop <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
getOverlapLoopNum <- function(loop, peak){
anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))
anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))
a <- queryHits(findOverlaps(anchor1, peak))
b <- queryHits(findOverlaps(anchor2, peak))
return(length(unique(c(a, b))))
}
getSEOverlapFisher <- function(allLoop, subsetLoop, peak){
all.overlap <- getOverlapLoopNum(allLoop, peak)
all.notOverlap <- nrow(allLoop) - all.overlap
subset.overlap <- getOverlapLoopNum(subsetLoop, peak)
subset.notOverlap <- nrow(subsetLoop) - subset.overlap
contingency_table <- matrix(c(subset.overlap, subset.notOverlap,
all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)
colnames(contingency_table) <- c(\Overlapping\, \Not_Overlapping\)
rownames(contingency_table) <- c(\All loops\, \Subset loops\)
# Perform Fisher's Exact Test
fisher_test_result <- fisher.test(contingency_table)
return(fisher_test_result)
}
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
loop.upno <- bind_rows(loop.up, loop.no)
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
### Dylan
# Seeding
temp <- getSEOverlapFisher(loop, loop.upno, peak.Dylan.SE)
result.tb <- tibble(loopType = \UP/NO\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# temp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)
# result.tb <- tibble(loopType = \UP\,
# target = \Dylan SE\,
# pvalue = temp$p.value,
# oddsRatio = temp$estimate)
# Add row
# temp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)
# result.tb <- result.tb %>%
# add_row(loopType = \NO\,
# target = \Dylan SE\,
# pvalue = temp$p.value,
# oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
### Whyte
# Seeding
# temp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)
# result.tb <- result.tb %>%
# add_row(loopType = \UP\,
# target = \Whyte SE\,
# pvalue = temp$p.value,
# oddsRatio = temp$estimate)
# Add row
temp <- getSEOverlapFisher(loop, loop.upno, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \UP/NO\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# Visualization
library(circlize)
data <- result.tb
heatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%
pivot_wider(names_from = loopType, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%
pivot_wider(names_from = loopType, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 2),
c(\#4852A0\, \white\, \#CB333A\))
p <- Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = fontSizeS, fontfamily = fontType))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
row_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),
column_names_gp = gpar(fontsize = fontSizeS, fontfamily = fontType),
heatmap_legend_param = list(
at = c(0, 1, 2),
labels = c(\0\, \1\, \2\),
title_gp = gpar(fontfamily = fontType, fontsize = fontSizeS),
labels_gp = gpar(fontfamily = fontType, fontsize = fontSizeS)
)
)
# fileName <- here(figDir, \heatmap_SE_enrichment\)
# width <- panelSize(1.5)*mmToInch
# height <- panelSize(0.7)*mmToInch
# svglite(paste0(fileName, \.svg\), width = width, height =height)
# print(p)
# dev.off()
# png(paste0(fileName, \.png\), width = width, height =height, res = 600, unit = \in\)
# print(p)
# dev.off()
#######################
data$loopType <- factor(data$loopType, levels = c(\UP/NO\, \DOWN\))
data$target <- factor(data$target, levels = c(\Whyte SE\, \Dylan SE\))
p <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
scale_fill_gradientn(colors = c(\#4852A0\, \white\, \#CB333A\), # Define gradient colors
values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5),
#low = \white\, high = \#CB333A\,
# limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) +
labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \heatmap_SE_enrichment_dotplot\)
width <- panelSize(1.8)*mmToInch
height <- panelSize(1.1)*mmToInch
# png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# print(p)
# dev.off()
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.27] Overlap with SE?
#### Q1. How many SEs are covered with Micro-C loops?
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\nloop <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\\Overlapping\\, \\Not_Overlapping\\)\n  rownames(contingency_table) <- c(\\All loops\\, \\Subset loops\\)\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\n\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\\))\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\\))\n\n### Dylan\n# Seeding\ntemp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)\nresult.tb <- tibble(loopType = \\UP\\,\n                    target = \\Dylan SE\\,\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n# Add row\ntemp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\NO\\,\n          target = \\Dylan SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Dylan SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n### Whyte\n# Seeding\ntemp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\UP\\,\n                    target = \\Whyte SE\\,\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n# Add row\ntemp <- getSEOverlapFisher(loop, loop.no, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\NO\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\n# Visualization\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 5), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\, \\4\\, \\5\\)))\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
loop <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
getOverlapLoopNum <- function(loop, peak){
anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))
anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))
a <- queryHits(findOverlaps(anchor1, peak))
b <- queryHits(findOverlaps(anchor2, peak))
return(length(unique(c(a, b))))
}
getSEOverlapFisher <- function(allLoop, subsetLoop, peak){
all.overlap <- getOverlapLoopNum(allLoop, peak)
all.notOverlap <- nrow(allLoop) - all.overlap
subset.overlap <- getOverlapLoopNum(subsetLoop, peak)
subset.notOverlap <- nrow(subsetLoop) - subset.overlap
contingency_table <- matrix(c(subset.overlap, subset.notOverlap,
all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)
colnames(contingency_table) <- c(\Overlapping\, \Not_Overlapping\)
rownames(contingency_table) <- c(\All loops\, \Subset loops\)
# Perform Fisher's Exact Test
fisher_test_result <- fisher.test(contingency_table)
return(fisher_test_result)
}
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
### Dylan
# Seeding
temp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)
result.tb <- tibble(loopType = \UP\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# Add row
temp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)
result.tb <- result.tb %>%
add_row(loopType = \NO\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
### Whyte
# Seeding
temp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \UP\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# Add row
temp <- getSEOverlapFisher(loop, loop.no, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \NO\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# Visualization
library(circlize)
data <- result.tb
heatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%
pivot_wider(names_from = loopType, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%
pivot_wider(names_from = loopType, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 5),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5),
labels = c(\0\, \1\, \2\, \3\, \4\, \5\)))
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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\nloop <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\\Overlapping\\, \\Not_Overlapping\\)\n  rownames(contingency_table) <- c(\\All loops\\, \\Subset loops\\)\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\n\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\\))\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\\))\n\n### Dylan\n# Seeding\ntemp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)\nresult.tb <- tibble(loopType = \\UP\\,\n                    target = \\Dylan SE\\,\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n# Add row\ntemp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\NO\\,\n          target = \\Dylan SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Dylan SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n### Whyte\n# Seeding\ntemp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\UP\\,\n                    target = \\Whyte SE\\,\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\n# Add row\ntemp <- getSEOverlapFisher(loop, loop.no, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\NO\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\ntemp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target = \\Whyte SE\\,\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\n# Visualization\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\ncol_fun <- colorRamp2(c(0, 1, 5), \n                      c(\\blue\\, \\white\\, \\red\\))\n\nHeatmap(as.matrix(heatmap_data),\n        name = \\Odds Ratio\\,\n        col = col_fun,\n        # Add annotation for p-values\n        cell_fun = function(j, i, x, y, width, height, fill) {\n          pval <- pvalue_data[i, j]\n          label <- ifelse(pval > 0.05, \\n.s.\\, sprintf(\\%.2e\\, pval))\n          grid.text(label, x, y, gp = gpar(fontsize = 10))\n        },\n        # Customize the heatmap layout\n        cluster_rows = TRUE,\n        show_row_dend = FALSE,\n        cluster_columns = FALSE,\n        column_title = \\Interest\\,\n        row_title = \\Target\\,\n        heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5), \n                                    labels = c(\\0\\, \\1\\, \\2\\, \\3\\, \\4\\, \\5\\)))\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
loop <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
getOverlapLoopNum <- function(loop, peak){
anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))
anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))
a <- queryHits(findOverlaps(anchor1, peak))
b <- queryHits(findOverlaps(anchor2, peak))
return(length(unique(c(a, b))))
}
getSEOverlapFisher <- function(allLoop, subsetLoop, peak){
all.overlap <- getOverlapLoopNum(allLoop, peak)
all.notOverlap <- nrow(allLoop) - all.overlap
subset.overlap <- getOverlapLoopNum(subsetLoop, peak)
subset.notOverlap <- nrow(subsetLoop) - subset.overlap
contingency_table <- matrix(c(subset.overlap, subset.notOverlap,
all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)
colnames(contingency_table) <- c(\Overlapping\, \Not_Overlapping\)
rownames(contingency_table) <- c(\All loops\, \Subset loops\)
# Perform Fisher's Exact Test
fisher_test_result <- fisher.test(contingency_table)
return(fisher_test_result)
}
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
### Dylan
# Seeding
temp <- getSEOverlapFisher(loop, loop.up, peak.Dylan.SE)
result.tb <- tibble(loopType = \UP\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# Add row
temp <- getSEOverlapFisher(loop, loop.no, peak.Dylan.SE)
result.tb <- result.tb %>%
add_row(loopType = \NO\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, peak.Dylan.SE)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = \Dylan SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
### Whyte
# Seeding
temp <- getSEOverlapFisher(loop, loop.up, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \UP\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# Add row
temp <- getSEOverlapFisher(loop, loop.no, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \NO\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, peak.Whyte.SE)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = \Whyte SE\,
pvalue = temp$p.value,
oddsRatio = temp$estimate)
# Visualization
library(circlize)
data <- result.tb
heatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%
pivot_wider(names_from = loopType, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%
pivot_wider(names_from = loopType, values_from = pvalue) %>%
column_to_rownames(var = \target\)
col_fun <- colorRamp2(c(0, 1, 5),
c(\blue\, \white\, \red\))
Heatmap(as.matrix(heatmap_data),
name = \Odds Ratio\,
col = col_fun,
# Add annotation for p-values
cell_fun = function(j, i, x, y, width, height, fill) {
pval <- pvalue_data[i, j]
label <- ifelse(pval > 0.05, \n.s.\, sprintf(\%.2e\, pval))
grid.text(label, x, y, gp = gpar(fontsize = 10))
},
# Customize the heatmap layout
cluster_rows = TRUE,
show_row_dend = FALSE,
cluster_columns = FALSE,
column_title = \Interest\,
row_title = \Target\,
heatmap_legend_param = list(at = c(0, 1, 2, 3, 4, 5),
labels = c(\0\, \1\, \2\, \3\, \4\, \5\)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Q2. Fisher's exact test?
##### dTAG
<!-- rnb-text-end -->
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diffCutoff <- 0.2
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
temp <- data
temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)
temp <- temp %>% dplyr::arrange(density)
p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
geom_hline(yintercept = diffCutoff, alpha = 1, color = \black\) +
geom_hline(yintercept = -diffCutoff, alpha = 1, color = \black\) +
geom_vline(xintercept = diffCutoff, alpha = 1, color = \black\) +
geom_vline(xintercept = -diffCutoff, alpha = 1, color = \black\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) +
theme_classic()
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_diff_\, loopName, \_dTAG-\, diffName)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 3.5, height = 3)
print(p1)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
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```r
```r
diffCutoff <- 0.2
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
temp <- data
temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)
temp <- temp %>% dplyr::arrange(density)
p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
geom_hline(yintercept = diffCutoff, alpha = 1, color = \black\) +
geom_hline(yintercept = -diffCutoff, alpha = 1, color = \black\) +
geom_vline(xintercept = diffCutoff, alpha = 1, color = \black\) +
geom_vline(xintercept = -diffCutoff, alpha = 1, color = \black\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) +
theme_classic()
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_diff_\, loopName, \_dTAG-\, diffName)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 3.5, height = 3)
print(p1)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
```
```
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##### A485
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 -->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ndata <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\n\nmakeAcrossSampleScatterplotdTAG <- function(data, AnnoList, diff, name, loopName, diffName){\n  temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,\n                                 updown_dTAG_DMSO %in% diff)\n  temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)\n  temp <- temp %>% dplyr::arrange(density)\n\n  p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +\n    geom_point(show.legend = FALSE) + \n    scale_color_viridis() + \n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) + \n    theme_classic() + ggtitle(paste0(name, \\_\\, loopName, \\_dTAG-\\, diffName)) + theme(plot.title = element_text(size = 5))\n  \n  fileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_diff_\\, loopName, \\_dTAG-\\, diffName)\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n}\n\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\all\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\UP\\), name, \\all\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\NO\\), name, \\all\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\DOWN\\), name, \\all\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\pe-pe\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\), name, \\pe-pe\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\NO\\), name, \\pe-pe\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\DOWN\\), name, \\pe-pe\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\str\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\UP\\), name, \\str\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\NO\\), name, \\str\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\DOWN\\), name, \\str\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485 <- function(data, AnnoList, diff, name, loopName, diffName){\n  temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,\n                                 updown_A485_DMSO %in% diff)\n  temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)\n  temp <- temp %>% dplyr::arrange(density)\n\n  p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +\n    geom_point(show.legend = FALSE) + \n    scale_color_viridis() + \n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) + \n    theme_classic() + ggtitle(paste0(name, \\_\\, loopName, \\_A485G-\\, diffName)) + theme(plot.title = element_text(size = 5))\n  \n  fileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_diff_\\, loopName, \\_A485-\\, diffName)\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n}\n\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\all\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\UP\\), name, \\all\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\NO\\), name, \\all\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\DOWN\\), name, \\all\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\pe-pe\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\), name, \\pe-pe\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\NO\\), name, \\pe-pe\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\DOWN\\), name, \\pe-pe\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\str\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\UP\\), name, \\str\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\NO\\), name, \\str\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\DOWN\\), name, \\str\\, \\DOWN\\)\n\n######## Barplot\ntemp <- data %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)\nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO) \nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\S-S\\, \\S-X\\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO) \nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n###\ntemp <- data %>% dplyr::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\S-S\\, \\S-X\\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
makeAcrossSampleScatterplotdTAG <- function(data, AnnoList, diff, name, loopName, diffName){
temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,
updown_dTAG_DMSO %in% diff)
temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)
temp <- temp %>% dplyr::arrange(density)
p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) +
theme_classic() + ggtitle(paste0(name, \_\, loopName, \_dTAG-\, diffName)) + theme(plot.title = element_text(size = 5))
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_diff_\, loopName, \_dTAG-\, diffName)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 3.5, height = 3)
print(p1)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
}
makeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\UP\, \NO\, \DOWN\), name, \all\, \all\)
makeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\UP\), name, \all\, \UP\)
makeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\NO\), name, \all\, \NO\)
makeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\DOWN\), name, \all\, \DOWN\)
makeAcrossSampleScatterplotdTAG(data, c(\P-P\, \P-E\, \E-E\), c(\UP\, \NO\, \DOWN\), name, \pe-pe\, \all\)
makeAcrossSampleScatterplotdTAG(data, c(\P-P\, \P-E\, \E-E\), c(\UP\), name, \pe-pe\, \UP\)
makeAcrossSampleScatterplotdTAG(data, c(\P-P\, \P-E\, \E-E\), c(\NO\), name, \pe-pe\, \NO\)
makeAcrossSampleScatterplotdTAG(data, c(\P-P\, \P-E\, \E-E\), c(\DOWN\), name, \pe-pe\, \DOWN\)
makeAcrossSampleScatterplotdTAG(data, c(\S-S\, \S-X\), c(\UP\, \NO\, \DOWN\), name, \str\, \all\)
makeAcrossSampleScatterplotdTAG(data, c(\S-S\, \S-X\), c(\UP\), name, \str\, \UP\)
makeAcrossSampleScatterplotdTAG(data, c(\S-S\, \S-X\), c(\NO\), name, \str\, \NO\)
makeAcrossSampleScatterplotdTAG(data, c(\S-S\, \S-X\), c(\DOWN\), name, \str\, \DOWN\)
makeAcrossSampleScatterplotA485 <- function(data, AnnoList, diff, name, loopName, diffName){
temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,
updown_A485_DMSO %in% diff)
temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)
temp <- temp %>% dplyr::arrange(density)
p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) +
theme_classic() + ggtitle(paste0(name, \_\, loopName, \_A485G-\, diffName)) + theme(plot.title = element_text(size = 5))
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_diff_\, loopName, \_A485-\, diffName)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 3.5, height = 3)
print(p1)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
}
makeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\UP\, \NO\, \DOWN\), name, \all\, \all\)
makeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\UP\), name, \all\, \UP\)
makeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\NO\), name, \all\, \NO\)
makeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\DOWN\), name, \all\, \DOWN\)
makeAcrossSampleScatterplotA485(data, c(\P-P\, \P-E\, \E-E\), c(\UP\, \NO\, \DOWN\), name, \pe-pe\, \all\)
makeAcrossSampleScatterplotA485(data, c(\P-P\, \P-E\, \E-E\), c(\UP\), name, \pe-pe\, \UP\)
makeAcrossSampleScatterplotA485(data, c(\P-P\, \P-E\, \E-E\), c(\NO\), name, \pe-pe\, \NO\)
makeAcrossSampleScatterplotA485(data, c(\P-P\, \P-E\, \E-E\), c(\DOWN\), name, \pe-pe\, \DOWN\)
makeAcrossSampleScatterplotA485(data, c(\S-S\, \S-X\), c(\UP\, \NO\, \DOWN\), name, \str\, \all\)
makeAcrossSampleScatterplotA485(data, c(\S-S\, \S-X\), c(\UP\), name, \str\, \UP\)
makeAcrossSampleScatterplotA485(data, c(\S-S\, \S-X\), c(\NO\), name, \str\, \NO\)
makeAcrossSampleScatterplotA485(data, c(\S-S\, \S-X\), c(\DOWN\), name, \str\, \DOWN\)
######## Barplot
temp <- data %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)
ggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
temp <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)
ggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
temp <- data %>% dplyr::filter(Anno2 %in% c(\S-S\, \S-X\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)
ggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
###
temp <- data %>% dplyr::select(updown_A485_DMSO, diff_dTAG_DMSO)
ggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
temp <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)
ggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
temp <- data %>% dplyr::filter(Anno2 %in% c(\S-S\, \S-X\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)
ggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ndata <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\n\nmakeAcrossSampleScatterplotdTAG <- function(data, AnnoList, diff, name, loopName, diffName){\n  temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,\n                                 updown_dTAG_DMSO %in% diff)\n  temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)\n  temp <- temp %>% dplyr::arrange(density)\n\n  p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +\n    geom_point(show.legend = FALSE) + \n    scale_color_viridis() + \n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) + \n    theme_classic() + ggtitle(paste0(name, \\_\\, loopName, \\_dTAG-\\, diffName)) + theme(plot.title = element_text(size = 5))\n  \n  fileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_diff_\\, loopName, \\_dTAG-\\, diffName)\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n}\n\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\all\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\UP\\), name, \\all\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\NO\\), name, \\all\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\DOWN\\), name, \\all\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\pe-pe\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\), name, \\pe-pe\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\NO\\), name, \\pe-pe\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\DOWN\\), name, \\pe-pe\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\str\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\UP\\), name, \\str\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\NO\\), name, \\str\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\DOWN\\), name, \\str\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485 <- function(data, AnnoList, diff, name, loopName, diffName){\n  temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,\n                                 updown_A485_DMSO %in% diff)\n  temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)\n  temp <- temp %>% dplyr::arrange(density)\n\n  p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +\n    geom_point(show.legend = FALSE) + \n    scale_color_viridis() + \n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) + \n    theme_classic() + ggtitle(paste0(name, \\_\\, loopName, \\_A485G-\\, diffName)) + theme(plot.title = element_text(size = 5))\n  \n  fileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_diff_\\, loopName, \\_A485-\\, diffName)\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n}\n\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\all\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\UP\\), name, \\all\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\NO\\), name, \\all\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\DOWN\\), name, \\all\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\pe-pe\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\), name, \\pe-pe\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\NO\\), name, \\pe-pe\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\DOWN\\), name, \\pe-pe\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\str\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\UP\\), name, \\str\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\NO\\), name, \\str\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\DOWN\\), name, \\str\\, \\DOWN\\)\n\n######## Barplot\ntemp <- data %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)\nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO) \nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\S-S\\, \\S-X\\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO) \nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n###\ntemp <- data %>% dplyr::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\S-S\\, \\S-X\\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ndata <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_dTAG_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)),\n                  updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \\UP\\, \n                                            ifelse(diff_A485_DMSO > -diffCutoff, \\NO\\, \\DOWN\\)))\n\nmakeAcrossSampleScatterplotdTAG <- function(data, AnnoList, diff, name, loopName, diffName){\n  temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,\n                                 updown_dTAG_DMSO %in% diff)\n  temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)\n  temp <- temp %>% dplyr::arrange(density)\n\n  p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +\n    geom_point(show.legend = FALSE) + \n    scale_color_viridis() + \n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) + \n    theme_classic() + ggtitle(paste0(name, \\_\\, loopName, \\_dTAG-\\, diffName)) + theme(plot.title = element_text(size = 5))\n  \n  fileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_diff_\\, loopName, \\_dTAG-\\, diffName)\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n}\n\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\all\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\UP\\), name, \\all\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\NO\\), name, \\all\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\\DOWN\\), name, \\all\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\pe-pe\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\), name, \\pe-pe\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\NO\\), name, \\pe-pe\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\DOWN\\), name, \\pe-pe\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\str\\, \\all\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\UP\\), name, \\str\\, \\UP\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\NO\\), name, \\str\\, \\NO\\)\nmakeAcrossSampleScatterplotdTAG(data, c(\\S-S\\, \\S-X\\), c(\\DOWN\\), name, \\str\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485 <- function(data, AnnoList, diff, name, loopName, diffName){\n  temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,\n                                 updown_A485_DMSO %in% diff)\n  temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)\n  temp <- temp %>% dplyr::arrange(density)\n\n  p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +\n    geom_point(show.legend = FALSE) + \n    scale_color_viridis() + \n    geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \\grey\\, linetype = \\dotted\\) +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) + \n    theme_classic() + ggtitle(paste0(name, \\_\\, loopName, \\_A485G-\\, diffName)) + theme(plot.title = element_text(size = 5))\n  \n  fileName <- paste0(\\scatterplot_\\, name, \\_\\, diffCutoff, \\_diff_\\, loopName, \\_A485-\\, diffName)\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, units = \\in\\, width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)), \n          width = 3.5, height = 3)\n  print(p1)\n  dev.off()\n}\n\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\all\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\UP\\), name, \\all\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\NO\\), name, \\all\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\\DOWN\\), name, \\all\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\pe-pe\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\UP\\), name, \\pe-pe\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\NO\\), name, \\pe-pe\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, c(\\P-P\\, \\P-E\\, \\E-E\\), c(\\DOWN\\), name, \\pe-pe\\, \\DOWN\\)\n\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\UP\\, \\NO\\, \\DOWN\\), name, \\str\\, \\all\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\UP\\), name, \\str\\, \\UP\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\NO\\), name, \\str\\, \\NO\\)\nmakeAcrossSampleScatterplotA485(data, c(\\S-S\\, \\S-X\\), c(\\DOWN\\), name, \\str\\, \\DOWN\\)\n\n######## Barplot\ntemp <- data %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)\nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO) \nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\S-S\\, \\S-X\\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO) \nggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n###\ntemp <- data %>% dplyr::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n\ntemp <- data %>% dplyr::filter(Anno2 %in% c(\\S-S\\, \\S-X\\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)\nggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +\n  geom_hline(yintercept = 0) +\n  geom_hline(yintercept = 0.2, linetype = \\dashed\\) +\n  geom_hline(yintercept = -0.2, linetype = \\dashed\\) + coord_cartesian(ylim = c(-0.5, 0.5))\n\n```\n```"} -->
```r
```r
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(diff_A485_DMSO > diffCutoff, \UP\,
ifelse(diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\)))
makeAcrossSampleScatterplotdTAG <- function(data, AnnoList, diff, name, loopName, diffName){
temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,
updown_dTAG_DMSO %in% diff)
temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)
temp <- temp %>% dplyr::arrange(density)
p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) +
theme_classic() + ggtitle(paste0(name, \_\, loopName, \_dTAG-\, diffName)) + theme(plot.title = element_text(size = 5))
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_diff_\, loopName, \_dTAG-\, diffName)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 3.5, height = 3)
print(p1)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
}
makeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\UP\, \NO\, \DOWN\), name, \all\, \all\)
makeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\UP\), name, \all\, \UP\)
makeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\NO\), name, \all\, \NO\)
makeAcrossSampleScatterplotdTAG(data, unique(data$Anno2), c(\DOWN\), name, \all\, \DOWN\)
makeAcrossSampleScatterplotdTAG(data, c(\P-P\, \P-E\, \E-E\), c(\UP\, \NO\, \DOWN\), name, \pe-pe\, \all\)
makeAcrossSampleScatterplotdTAG(data, c(\P-P\, \P-E\, \E-E\), c(\UP\), name, \pe-pe\, \UP\)
makeAcrossSampleScatterplotdTAG(data, c(\P-P\, \P-E\, \E-E\), c(\NO\), name, \pe-pe\, \NO\)
makeAcrossSampleScatterplotdTAG(data, c(\P-P\, \P-E\, \E-E\), c(\DOWN\), name, \pe-pe\, \DOWN\)
makeAcrossSampleScatterplotdTAG(data, c(\S-S\, \S-X\), c(\UP\, \NO\, \DOWN\), name, \str\, \all\)
makeAcrossSampleScatterplotdTAG(data, c(\S-S\, \S-X\), c(\UP\), name, \str\, \UP\)
makeAcrossSampleScatterplotdTAG(data, c(\S-S\, \S-X\), c(\NO\), name, \str\, \NO\)
makeAcrossSampleScatterplotdTAG(data, c(\S-S\, \S-X\), c(\DOWN\), name, \str\, \DOWN\)
makeAcrossSampleScatterplotA485 <- function(data, AnnoList, diff, name, loopName, diffName){
temp <- data %>% dplyr::filter(Anno2 %in% AnnoList,
updown_A485_DMSO %in% diff)
temp$density <- get_density(temp$diff_dTAG_DMSO, temp$diff_A485_DMSO, n = 100)
temp <- temp %>% dplyr::arrange(density)
p1 <- ggplot(temp, aes(x = diff_dTAG_DMSO, y = diff_A485_DMSO, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +coord_fixed(ratio = 1, ylim = c(-1, 1), xlim = c(-1, 1)) +
theme_classic() + ggtitle(paste0(name, \_\, loopName, \_A485G-\, diffName)) + theme(plot.title = element_text(size = 5))
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff, \_diff_\, loopName, \_A485-\, diffName)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 3.5, height = 3)
print(p1)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 3.5, height = 3)
print(p1)
dev.off()
}
makeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\UP\, \NO\, \DOWN\), name, \all\, \all\)
makeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\UP\), name, \all\, \UP\)
makeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\NO\), name, \all\, \NO\)
makeAcrossSampleScatterplotA485(data, unique(data$Anno2), c(\DOWN\), name, \all\, \DOWN\)
makeAcrossSampleScatterplotA485(data, c(\P-P\, \P-E\, \E-E\), c(\UP\, \NO\, \DOWN\), name, \pe-pe\, \all\)
makeAcrossSampleScatterplotA485(data, c(\P-P\, \P-E\, \E-E\), c(\UP\), name, \pe-pe\, \UP\)
makeAcrossSampleScatterplotA485(data, c(\P-P\, \P-E\, \E-E\), c(\NO\), name, \pe-pe\, \NO\)
makeAcrossSampleScatterplotA485(data, c(\P-P\, \P-E\, \E-E\), c(\DOWN\), name, \pe-pe\, \DOWN\)
makeAcrossSampleScatterplotA485(data, c(\S-S\, \S-X\), c(\UP\, \NO\, \DOWN\), name, \str\, \all\)
makeAcrossSampleScatterplotA485(data, c(\S-S\, \S-X\), c(\UP\), name, \str\, \UP\)
makeAcrossSampleScatterplotA485(data, c(\S-S\, \S-X\), c(\NO\), name, \str\, \NO\)
makeAcrossSampleScatterplotA485(data, c(\S-S\, \S-X\), c(\DOWN\), name, \str\, \DOWN\)
######## Barplot
temp <- data %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)
ggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
temp <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)
ggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
temp <- data %>% dplyr::filter(Anno2 %in% c(\S-S\, \S-X\)) %>% dplyr::select(updown_dTAG_DMSO, diff_A485_DMSO)
ggplot(temp, aes(x = updown_dTAG_DMSO, y = diff_A485_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
###
temp <- data %>% dplyr::select(updown_A485_DMSO, diff_dTAG_DMSO)
ggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
temp <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)
ggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
temp <- data %>% dplyr::filter(Anno2 %in% c(\S-S\, \S-X\)) %>% dplyr:::select(updown_A485_DMSO, diff_dTAG_DMSO)
ggplot(temp, aes(x = updown_A485_DMSO, y = diff_dTAG_DMSO)) + geom_boxplot(outlier.shape = NA) + theme_classic() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 0.2, linetype = \dashed\) +
geom_hline(yintercept = -0.2, linetype = \dashed\) + coord_cartesian(ylim = c(-0.5, 0.5))
```
```
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## [2.28] Comparing differential loops in dTAG to A485
The question I want to ask here is whether perturbed loops in dTAG experiments are either perturbed or not perturbed in A485 experiment. If there is compensation going on between RAD21 and A485, UP loop in RAD21 should be more DOWN in A485 and vice versa
#### Exploratory part
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-->\n\n```r\n### IMPORTING COMPARMTNET SCORES\ncompDir <- here(\\../..\\, \\result\\, \\compartment\\, \\CscoreTools\\)\n\ncscore.DMSO <- as_tibble(fread(here(compDir, \\G1DMSO_Merged_10kb_cscore_final.bedgraph\\), skip = 1)) %>%\n  dplyr::mutate(V2 = V2 + 1)\ncolnames(cscore.DMSO) <- c(\\chr\\, \\start\\, \\end\\, \\cscore_DMSO\\)\n\ncscore.A485 <- as_tibble(fread(here(compDir, \\G1A485_Merged_10kb_cscore_final.bedgraph\\), skip = 1)) %>%\n  dplyr::mutate(V2 = V2 + 1)\ncolnames(cscore.A485) <- c(\\chr\\, \\start\\, \\end\\, \\cscore_A485\\)\n\n\ncscore <- dplyr::full_join(cscore.DMSO, cscore.A485, by = c(\\chr\\, \\start\\, \\end\\))\n\n# FILTER ROWS WITH NA\ncscore <- cscore %>% filter(!if_any(everything(), is.na))\n\n# Annotating how the compartment changed\nthreshold <- 0.1\ncscore <- cscore %>% rowwise() %>% dplyr::mutate(isAB_DMSO = ifelse(cscore_DMSO > 0, \\A\\, \\B\\),\n                                                 isAB_A485 = ifelse(cscore_A485 > 0, \\A\\, \\B\\),\n                                                 changeType = case_when(\n                                                   abs(cscore_DMSO - cscore_A485) < threshold ~ \\Unchanged\\,\n                                                   isAB_DMSO == \\A\\ & isAB_A485 == \\B\\ ~ \\AtoB\\,\n                                                   isAB_DMSO == \\B\\ & isAB_A485 == \\A\\ ~ \\BtoA\\,\n                                                   isAB_DMSO == \\A\\ & isAB_A485 == \\A\\ & abs(cscore_DMSO) > abs(cscore_A485) ~ \\A_weakening\\,\n                                                   isAB_DMSO == \\A\\ & isAB_A485 == \\A\\ & abs(cscore_DMSO) <= abs(cscore_A485) ~ \\A_strengthening\\,\n                                                   isAB_DMSO == \\B\\ & isAB_A485 == \\B\\ & abs(cscore_DMSO) > abs(cscore_A485) ~ \\B_weakening\\,\n                                                   isAB_DMSO == \\B\\ & isAB_A485 == \\B\\ & abs(cscore_DMSO) <= abs(cscore_A485) ~ \\B_strengthening\\                                                 ))\ncscore_summary <- tibble(comparison = rep(\\A485_vs_DMSO\\, 7),\n                         changeType = c(\\AtoB\\, \\BtoA\\, \\A_weakening\\, \\A_strengthening\\, \\B_weakening\\, \\B_strengthening\\, \\Unchanged\\),\n                         value = c(sum(cscore$changeType == \\AtoB\\),\n                                   sum(cscore$changeType == \\BtoA\\),\n                                   sum(cscore$changeType == \\A_weakening\\),\n                                   sum(cscore$changeType == \\A_strengthening\\),\n                                   sum(cscore$changeType == \\B_weakening\\),\n                                   sum(cscore$changeType == \\B_strengthening\\),\n                                   sum(cscore$changeType == \\Unchanged\\)))\ncscore_summary$changeType <- factor(cscore_summary$changeType, levels = c(\\A_weakening\\, \\AtoB\\, \\B_strengthening\\,\n                                                                          \\B_weakening\\, \\BtoA\\, \\A_strengthening\\,\n                                                                          \\Unchanged\\))\nggplot(cscore_summary, aes(x = comparison, y = value, fill = changeType)) + geom_bar(position = \\stack\\, stat = \\identity\\) +\n  theme_bw() +\n  scale_fill_manual(values = c(\\skyblue\\, \\blue\\, \\darkblue\\, \\pink\\, \\red2\\, \\darkred\\ ,\\grey\\))\n  \n```\n\n<!-- rnb-source-end -->\n"} -->
### IMPORTING COMPARMTNET SCORES
compDir <- here(\../..\, \result\, \compartment\, \CscoreTools\)
cscore.DMSO <- as_tibble(fread(here(compDir, \G1DMSO_Merged_10kb_cscore_final.bedgraph\), skip = 1)) %>%
dplyr::mutate(V2 = V2 + 1)
colnames(cscore.DMSO) <- c(\chr\, \start\, \end\, \cscore_DMSO\)
cscore.A485 <- as_tibble(fread(here(compDir, \G1A485_Merged_10kb_cscore_final.bedgraph\), skip = 1)) %>%
dplyr::mutate(V2 = V2 + 1)
colnames(cscore.A485) <- c(\chr\, \start\, \end\, \cscore_A485\)
cscore <- dplyr::full_join(cscore.DMSO, cscore.A485, by = c(\chr\, \start\, \end\))
# FILTER ROWS WITH NA
cscore <- cscore %>% filter(!if_any(everything(), is.na))
# Annotating how the compartment changed
threshold <- 0.1
cscore <- cscore %>% rowwise() %>% dplyr::mutate(isAB_DMSO = ifelse(cscore_DMSO > 0, \A\, \B\),
isAB_A485 = ifelse(cscore_A485 > 0, \A\, \B\),
changeType = case_when(
abs(cscore_DMSO - cscore_A485) < threshold ~ \Unchanged\,
isAB_DMSO == \A\ & isAB_A485 == \B\ ~ \AtoB\,
isAB_DMSO == \B\ & isAB_A485 == \A\ ~ \BtoA\,
isAB_DMSO == \A\ & isAB_A485 == \A\ & abs(cscore_DMSO) > abs(cscore_A485) ~ \A_weakening\,
isAB_DMSO == \A\ & isAB_A485 == \A\ & abs(cscore_DMSO) <= abs(cscore_A485) ~ \A_strengthening\,
isAB_DMSO == \B\ & isAB_A485 == \B\ & abs(cscore_DMSO) > abs(cscore_A485) ~ \B_weakening\,
isAB_DMSO == \B\ & isAB_A485 == \B\ & abs(cscore_DMSO) <= abs(cscore_A485) ~ \B_strengthening\ ))
cscore_summary <- tibble(comparison = rep(\A485_vs_DMSO\, 7),
changeType = c(\AtoB\, \BtoA\, \A_weakening\, \A_strengthening\, \B_weakening\, \B_strengthening\, \Unchanged\),
value = c(sum(cscore$changeType == \AtoB\),
sum(cscore$changeType == \BtoA\),
sum(cscore$changeType == \A_weakening\),
sum(cscore$changeType == \A_strengthening\),
sum(cscore$changeType == \B_weakening\),
sum(cscore$changeType == \B_strengthening\),
sum(cscore$changeType == \Unchanged\)))
cscore_summary$changeType <- factor(cscore_summary$changeType, levels = c(\A_weakening\, \AtoB\, \B_strengthening\,
\B_weakening\, \BtoA\, \A_strengthening\,
\Unchanged\))
ggplot(cscore_summary, aes(x = comparison, y = value, fill = changeType)) + geom_bar(position = \stack\, stat = \identity\) +
theme_bw() +
scale_fill_manual(values = c(\skyblue\, \blue\, \darkblue\, \pink\, \red2\, \darkred\ ,\grey\))
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n### IMPORTING COMPARMTNET SCORES\ncompDir <- here(\\../..\\, \\result\\, \\compartment\\, \\CscoreTools\\)\n\ncscore.DMSO <- as_tibble(fread(here(compDir, \\G1DMSO_Merged_10kb_cscore_final.bedgraph\\), skip = 1)) %>%\n  dplyr::mutate(V2 = V2 + 1)\ncolnames(cscore.DMSO) <- c(\\chr\\, \\start\\, \\end\\, \\cscore_DMSO\\)\n\ncscore.A485 <- as_tibble(fread(here(compDir, \\G1A485_Merged_10kb_cscore_final.bedgraph\\), skip = 1)) %>%\n  dplyr::mutate(V2 = V2 + 1)\ncolnames(cscore.A485) <- c(\\chr\\, \\start\\, \\end\\, \\cscore_A485\\)\n\n\ncscore <- dplyr::full_join(cscore.DMSO, cscore.A485, by = c(\\chr\\, \\start\\, \\end\\))\n\n# FILTER ROWS WITH NA\ncscore <- cscore %>% filter(!if_any(everything(), is.na))\n\n# Annotating how the compartment changed\nthreshold <- 0.1\ncscore <- cscore %>% rowwise() %>% dplyr::mutate(isAB_DMSO = ifelse(cscore_DMSO > 0, \\A\\, \\B\\),\n                                                 isAB_A485 = ifelse(cscore_A485 > 0, \\A\\, \\B\\),\n                                                 changeType = case_when(\n                                                   abs(cscore_DMSO - cscore_A485) < threshold ~ \\Unchanged\\,\n                                                   isAB_DMSO == \\A\\ & isAB_A485 == \\B\\ ~ \\AtoB\\,\n                                                   isAB_DMSO == \\B\\ & isAB_A485 == \\A\\ ~ \\BtoA\\,\n                                                   isAB_DMSO == \\A\\ & isAB_A485 == \\A\\ & abs(cscore_DMSO) > abs(cscore_A485) ~ \\A_weakening\\,\n                                                   isAB_DMSO == \\A\\ & isAB_A485 == \\A\\ & abs(cscore_DMSO) <= abs(cscore_A485) ~ \\A_strengthening\\,\n                                                   isAB_DMSO == \\B\\ & isAB_A485 == \\B\\ & abs(cscore_DMSO) > abs(cscore_A485) ~ \\B_weakening\\,\n                                                   isAB_DMSO == \\B\\ & isAB_A485 == \\B\\ & abs(cscore_DMSO) <= abs(cscore_A485) ~ \\B_strengthening\\                                                 ))\ncscore_summary <- tibble(comparison = rep(\\A485_vs_DMSO\\, 7),\n                         changeType = c(\\AtoB\\, \\BtoA\\, \\A_weakening\\, \\A_strengthening\\, \\B_weakening\\, \\B_strengthening\\, \\Unchanged\\),\n                         value = c(sum(cscore$changeType == \\AtoB\\),\n                                   sum(cscore$changeType == \\BtoA\\),\n                                   sum(cscore$changeType == \\A_weakening\\),\n                                   sum(cscore$changeType == \\A_strengthening\\),\n                                   sum(cscore$changeType == \\B_weakening\\),\n                                   sum(cscore$changeType == \\B_strengthening\\),\n                                   sum(cscore$changeType == \\Unchanged\\)))\ncscore_summary$changeType <- factor(cscore_summary$changeType, levels = c(\\A_weakening\\, \\AtoB\\, \\B_strengthening\\,\n                                                                          \\B_weakening\\, \\BtoA\\, \\A_strengthening\\,\n                                                                          \\Unchanged\\))\nggplot(cscore_summary, aes(x = comparison, y = value, fill = changeType)) + geom_bar(position = \\stack\\, stat = \\identity\\) +\n  theme_bw() +\n  scale_fill_manual(values = c(\\skyblue\\, \\blue\\, \\darkblue\\, \\pink\\, \\red2\\, \\darkred\\ ,\\grey\\))\n  \n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
### IMPORTING COMPARMTNET SCORES
compDir <- here(\../..\, \result\, \compartment\, \CscoreTools\)
cscore.DMSO <- as_tibble(fread(here(compDir, \G1DMSO_Merged_10kb_cscore_final.bedgraph\), skip = 1)) %>%
dplyr::mutate(V2 = V2 + 1)
colnames(cscore.DMSO) <- c(\chr\, \start\, \end\, \cscore_DMSO\)
cscore.A485 <- as_tibble(fread(here(compDir, \G1A485_Merged_10kb_cscore_final.bedgraph\), skip = 1)) %>%
dplyr::mutate(V2 = V2 + 1)
colnames(cscore.A485) <- c(\chr\, \start\, \end\, \cscore_A485\)
cscore <- dplyr::full_join(cscore.DMSO, cscore.A485, by = c(\chr\, \start\, \end\))
# FILTER ROWS WITH NA
cscore <- cscore %>% filter(!if_any(everything(), is.na))
# Annotating how the compartment changed
threshold <- 0.1
cscore <- cscore %>% rowwise() %>% dplyr::mutate(isAB_DMSO = ifelse(cscore_DMSO > 0, \A\, \B\),
isAB_A485 = ifelse(cscore_A485 > 0, \A\, \B\),
changeType = case_when(
abs(cscore_DMSO - cscore_A485) < threshold ~ \Unchanged\,
isAB_DMSO == \A\ & isAB_A485 == \B\ ~ \AtoB\,
isAB_DMSO == \B\ & isAB_A485 == \A\ ~ \BtoA\,
isAB_DMSO == \A\ & isAB_A485 == \A\ & abs(cscore_DMSO) > abs(cscore_A485) ~ \A_weakening\,
isAB_DMSO == \A\ & isAB_A485 == \A\ & abs(cscore_DMSO) <= abs(cscore_A485) ~ \A_strengthening\,
isAB_DMSO == \B\ & isAB_A485 == \B\ & abs(cscore_DMSO) > abs(cscore_A485) ~ \B_weakening\,
isAB_DMSO == \B\ & isAB_A485 == \B\ & abs(cscore_DMSO) <= abs(cscore_A485) ~ \B_strengthening\ ))
cscore_summary <- tibble(comparison = rep(\A485_vs_DMSO\, 7),
changeType = c(\AtoB\, \BtoA\, \A_weakening\, \A_strengthening\, \B_weakening\, \B_strengthening\, \Unchanged\),
value = c(sum(cscore$changeType == \AtoB\),
sum(cscore$changeType == \BtoA\),
sum(cscore$changeType == \A_weakening\),
sum(cscore$changeType == \A_strengthening\),
sum(cscore$changeType == \B_weakening\),
sum(cscore$changeType == \B_strengthening\),
sum(cscore$changeType == \Unchanged\)))
cscore_summary$changeType <- factor(cscore_summary$changeType, levels = c(\A_weakening\, \AtoB\, \B_strengthening\,
\B_weakening\, \BtoA\, \A_strengthening\,
\Unchanged\))
ggplot(cscore_summary, aes(x = comparison, y = value, fill = changeType)) + geom_bar(position = \stack\, stat = \identity\) +
theme_bw() +
scale_fill_manual(values = c(\skyblue\, \blue\, \darkblue\, \pink\, \red2\, \darkred\ ,\grey\))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Scatterplot & box plot
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V1, TSS, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\TSS\\, \\ensembl\\)\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\n\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\n\ngetCompChangeType <- function(chrom, TSS, cscore.tb){\n  temp.tb <- cscore.tb %>% dplyr::filter(chr == chrom, start < TSS, end > TSS)\n  out <- temp.tb$changeType\n  if(length(out) < 1){\n    return(NA)\n  }else{\n    return(out)\n  }\n}\n\ncscore.gr <- makeGRangesFromDataFrame(cscore[1:3])\ntemp <-diff.RNA %>% dplyr::mutate(start = TSS, end = TSS +1) %>%\n  dplyr::select(chr, start, end)\ndiff.RNA.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(diff.RNA.gr, cscore.gr)\n\n#cscore.selected <- cscore %>% dplyr::slice(subjectHits(overlap))\n## Problem of certain genes not getting overlap with cscore due to sparse calling?\nbatch1 <- bind_cols(diff.RNA[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\nmissed <- diff.RNA[-queryHits(overlap)]\ntemp <- missed %>% dplyr::mutate(TSS = TSS + 10000) %>% dplyr::mutate(start = TSS, end = TSS +1) %>%\n  dplyr::select(chr, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch2 <- bind_cols(missed[queryHits(overlap)],\n                    data.table(cscore)[subjectHits(overlap)])\n\nmissed2 <- missed[-queryHits(overlap)]\ntemp <- missed2 %>% dplyr::mutate(TSS = TSS - 10000) %>% dplyr::mutate(start = TSS, end = TSS +1) %>%\n  dplyr::select(chr, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch3 <- bind_cols(missed2[queryHits(overlap)],\n                    data.table(cscore)[subjectHits(overlap)])\n\n#missed3 <- missed2[-queryHits(overlap)]\n\n\ndiff.RNA <- bind_rows(batch1, batch2, batch3)\n\n\n# VISUALIZE\ndiff.RNA$changeType <- factor(diff.RNA$changeType, levels = c(\\A_weakening\\, \\AtoB\\, \\B_strengthening\\,\n                                                                          \\B_weakening\\, \\BtoA\\, \\A_strengthening\\,\n                                                                          \\Unchanged\\))\nggplot(diff.RNA, aes(x = diff, fill = changeType)) +\n  geom_bar(position = \\fill\\) +\n  labs(title = \\Stacked Bar Plot of Change Type by Diff\\,\n       x = \\Diff\\,\n       y = \\Count\\) +\n  theme_bw() +  scale_fill_manual(values = c(\\skyblue\\, \\blue\\, \\darkblue\\, \\pink\\, \\red2\\, \\darkred\\ ,\\grey\\))\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V1, TSS, V6)
colnames(gene.tb) <- c(\chr\, \TSS\, \ensembl\)
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
getCompChangeType <- function(chrom, TSS, cscore.tb){
temp.tb <- cscore.tb %>% dplyr::filter(chr == chrom, start < TSS, end > TSS)
out <- temp.tb$changeType
if(length(out) < 1){
return(NA)
}else{
return(out)
}
}
cscore.gr <- makeGRangesFromDataFrame(cscore[1:3])
temp <-diff.RNA %>% dplyr::mutate(start = TSS, end = TSS +1) %>%
dplyr::select(chr, start, end)
diff.RNA.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(diff.RNA.gr, cscore.gr)
#cscore.selected <- cscore %>% dplyr::slice(subjectHits(overlap))
## Problem of certain genes not getting overlap with cscore due to sparse calling?
batch1 <- bind_cols(diff.RNA[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed <- diff.RNA[-queryHits(overlap)]
temp <- missed %>% dplyr::mutate(TSS = TSS + 10000) %>% dplyr::mutate(start = TSS, end = TSS +1) %>%
dplyr::select(chr, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch2 <- bind_cols(missed[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed2 <- missed[-queryHits(overlap)]
temp <- missed2 %>% dplyr::mutate(TSS = TSS - 10000) %>% dplyr::mutate(start = TSS, end = TSS +1) %>%
dplyr::select(chr, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch3 <- bind_cols(missed2[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
#missed3 <- missed2[-queryHits(overlap)]
diff.RNA <- bind_rows(batch1, batch2, batch3)
# VISUALIZE
diff.RNA$changeType <- factor(diff.RNA$changeType, levels = c(\A_weakening\, \AtoB\, \B_strengthening\,
\B_weakening\, \BtoA\, \A_strengthening\,
\Unchanged\))
ggplot(diff.RNA, aes(x = diff, fill = changeType)) +
geom_bar(position = \fill\) +
labs(title = \Stacked Bar Plot of Change Type by Diff\,
x = \Diff\,
y = \Count\) +
theme_bw() + scale_fill_manual(values = c(\skyblue\, \blue\, \darkblue\, \pink\, \red2\, \darkred\ ,\grey\))
<!-- rnb-output-end -->
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-->\n\n```r\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V1, TSS, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\TSS\\, \\ensembl\\)\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\ndiff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\\ensembl_gene_id\\ = \\ensembl\\)) %>%\n  dplyr::filter(!is.na(TSS))\n\nalpha <- 0.05\nfcCutoff <- 0.5\n\ndiff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \\UP\\,\n                                                        padj < alpha & shrinked_log2FC < -fcCutoff ~ \\DOWN\\,\n                                                        TRUE ~ \\NO\\))\n\n\ngetCompChangeType <- function(chrom, TSS, cscore.tb){\n  temp.tb <- cscore.tb %>% dplyr::filter(chr == chrom, start < TSS, end > TSS)\n  out <- temp.tb$changeType\n  if(length(out) < 1){\n    return(NA)\n  }else{\n    return(out)\n  }\n}\n\ncscore.gr <- makeGRangesFromDataFrame(cscore[1:3])\ntemp <-diff.RNA %>% dplyr::mutate(start = TSS, end = TSS +1) %>%\n  dplyr::select(chr, start, end)\ndiff.RNA.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(diff.RNA.gr, cscore.gr)\n\n#cscore.selected <- cscore %>% dplyr::slice(subjectHits(overlap))\n## Problem of certain genes not getting overlap with cscore due to sparse calling?\nbatch1 <- bind_cols(diff.RNA[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\nmissed <- diff.RNA[-queryHits(overlap)]\ntemp <- missed %>% dplyr::mutate(TSS = TSS + 10000) %>% dplyr::mutate(start = TSS, end = TSS +1) %>%\n  dplyr::select(chr, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch2 <- bind_cols(missed[queryHits(overlap)],\n                    data.table(cscore)[subjectHits(overlap)])\n\nmissed2 <- missed[-queryHits(overlap)]\ntemp <- missed2 %>% dplyr::mutate(TSS = TSS - 10000) %>% dplyr::mutate(start = TSS, end = TSS +1) %>%\n  dplyr::select(chr, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch3 <- bind_cols(missed2[queryHits(overlap)],\n                    data.table(cscore)[subjectHits(overlap)])\n\n#missed3 <- missed2[-queryHits(overlap)]\n\n\ndiff.RNA <- bind_rows(batch1, batch2, batch3)\n\n\n# VISUALIZE\ndiff.RNA$changeType <- factor(diff.RNA$changeType, levels = c(\\A_weakening\\, \\AtoB\\, \\B_strengthening\\,\n                                                                          \\B_weakening\\, \\BtoA\\, \\A_strengthening\\,\n                                                                          \\Unchanged\\))\nggplot(diff.RNA, aes(x = diff, fill = changeType)) +\n  geom_bar(position = \\fill\\) +\n  labs(title = \\Stacked Bar Plot of Change Type by Diff\\,\n       x = \\Diff\\,\n       y = \\Count\\) +\n  theme_bw() +  scale_fill_manual(values = c(\\skyblue\\, \\blue\\, \\darkblue\\, \\pink\\, \\red2\\, \\darkred\\ ,\\grey\\))\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V1, TSS, V6)
colnames(gene.tb) <- c(\chr\, \TSS\, \ensembl\)
diff.RNA <- fread(here(refDir, \diff_G1.A485.selected2_G1.2i.A485_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
diff.RNA <- diff.RNA %>% dplyr::left_join(gene.tb, by = c(\ensembl_gene_id\ = \ensembl\)) %>%
dplyr::filter(!is.na(TSS))
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA <- diff.RNA %>% dplyr::mutate(diff = case_when(padj < alpha & shrinked_log2FC > fcCutoff ~ \UP\,
padj < alpha & shrinked_log2FC < -fcCutoff ~ \DOWN\,
TRUE ~ \NO\))
getCompChangeType <- function(chrom, TSS, cscore.tb){
temp.tb <- cscore.tb %>% dplyr::filter(chr == chrom, start < TSS, end > TSS)
out <- temp.tb$changeType
if(length(out) < 1){
return(NA)
}else{
return(out)
}
}
cscore.gr <- makeGRangesFromDataFrame(cscore[1:3])
temp <-diff.RNA %>% dplyr::mutate(start = TSS, end = TSS +1) %>%
dplyr::select(chr, start, end)
diff.RNA.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(diff.RNA.gr, cscore.gr)
#cscore.selected <- cscore %>% dplyr::slice(subjectHits(overlap))
## Problem of certain genes not getting overlap with cscore due to sparse calling?
batch1 <- bind_cols(diff.RNA[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed <- diff.RNA[-queryHits(overlap)]
temp <- missed %>% dplyr::mutate(TSS = TSS + 10000) %>% dplyr::mutate(start = TSS, end = TSS +1) %>%
dplyr::select(chr, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch2 <- bind_cols(missed[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed2 <- missed[-queryHits(overlap)]
temp <- missed2 %>% dplyr::mutate(TSS = TSS - 10000) %>% dplyr::mutate(start = TSS, end = TSS +1) %>%
dplyr::select(chr, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch3 <- bind_cols(missed2[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
#missed3 <- missed2[-queryHits(overlap)]
diff.RNA <- bind_rows(batch1, batch2, batch3)
# VISUALIZE
diff.RNA$changeType <- factor(diff.RNA$changeType, levels = c(\A_weakening\, \AtoB\, \B_strengthening\,
\B_weakening\, \BtoA\, \A_strengthening\,
\Unchanged\))
ggplot(diff.RNA, aes(x = diff, fill = changeType)) +
geom_bar(position = \fill\) +
labs(title = \Stacked Bar Plot of Change Type by Diff\,
x = \Diff\,
y = \Count\) +
theme_bw() + scale_fill_manual(values = c(\skyblue\, \blue\, \darkblue\, \pink\, \red2\, \darkred\ ,\grey\))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
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## [2.29] Compartment analysis
Q. What are the changes in compartment level upon A485 treatment?
#### Compartment change distribution
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\\))\nanchor.up <- (extractAnchor(loop.up))\nanchor.up.tb <- as_tibble(anchor.up) %>%\n  dplyr::mutate(center = (start + end)/2) %>%\n  dplyr::mutate(start = center-1, end = center+1) %>%\n  dplyr::select(seqnames, start, end)\nanchor.up <- makeGRangesFromDataFrame(anchor.up.tb)\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\\))\nanchor.no <- (extractAnchor(loop.no))\nanchor.no.tb <- as_tibble(anchor.no) %>%\n  dplyr::mutate(center = (start + end)/2) %>%\n  dplyr::mutate(start = center-1, end = center+1) %>%\n  dplyr::select(seqnames, start, end)\nanchor.no <- makeGRangesFromDataFrame(anchor.no.tb)\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- (extractAnchor(loop.down))\nanchor.down.tb <- as_tibble(anchor.down) %>%\n  dplyr::mutate(center = (start + end)/2) %>%\n  dplyr::mutate(start = center-1, end = center+1) %>%\n  dplyr::select(seqnames, start, end)\nanchor.down <- makeGRangesFromDataFrame(anchor.down.tb)\n\n\n########################################################################\n### Overlap\ncscore.gr <- makeGRangesFromDataFrame(cscore[1:3])\noverlap <- findOverlaps(anchor.up, cscore.gr)\n\n## Problem of certain genes not getting overlap with cscore due to sparse calling?\nbatch1 <- bind_cols(data.table(anchor.up.tb)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\nmissed <- data.table(anchor.up.tb)[-queryHits(overlap)]\ntemp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%\n  dplyr::select(seqnames, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch2 <- bind_cols((missed)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\ndata.up <- bind_rows(batch1, batch2)\n\n########################################################################\n### Overlap\ncscore.gr <- makeGRangesFromDataFrame(cscore[1:3])\noverlap <- findOverlaps(anchor.no, cscore.gr)\n\n## Problem of certain genes not getting overlap with cscore due to sparse calling?\nbatch1 <- bind_cols(data.table(anchor.no.tb)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\nmissed <- data.table(anchor.no.tb)[-queryHits(overlap)]\ntemp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%\n  dplyr::select(seqnames, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch2 <- bind_cols((missed)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\ndata.no <- bind_rows(batch1, batch2)\n\n########################################################################\n### Overlap\ncscore.gr <- makeGRangesFromDataFrame(cscore[1:3])\noverlap <- findOverlaps(anchor.down, cscore.gr)\n\n## Problem of certain genes not getting overlap with cscore due to sparse calling?\nbatch1 <- bind_cols(data.table(anchor.down.tb)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\nmissed <- data.table(anchor.down.tb)[-queryHits(overlap)]\ntemp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%\n  dplyr::select(seqnames, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch2 <- bind_cols((missed)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\ndata.down <- bind_rows(batch1, batch2)\n\nn.up <- nrow(data.up)\nn.no <- nrow(data.no)\nn.down <- nrow(data.down)\n\ndata <- tibble(loopDiff = c(rep(\\UP\\, n.up), rep(\\NO\\, n.no), rep(\\DOWN\\, n.down)),\n              changeType = c(data.up$changeType, data.no$changeType, data.down$changeType))\n\n# VISUALIZE\ndata$changeType <- factor(data$changeType, levels = c(\\A_weakening\\, \\AtoB\\, \\B_strengthening\\,\n                                                                          \\B_weakening\\, \\BtoA\\, \\A_strengthening\\,\n                                                                          \\Unchanged\\))\nggplot(data, aes(x = loopDiff, fill = changeType)) +\n  geom_bar(position = \\fill\\) +\n  labs(title = \\Stacked Bar Plot of Change Type by Diff\\,\n       x = \\Diff\\,\n       y = \\Count\\) +\n  theme_bw() +  scale_fill_manual(values = c(\\skyblue\\, \\blue\\, \\darkblue\\, \\pink\\, \\red2\\, \\darkred\\ ,\\grey\\))\n\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
anchor.up.tb <- as_tibble(anchor.up) %>%
dplyr::mutate(center = (start + end)/2) %>%
dplyr::mutate(start = center-1, end = center+1) %>%
dplyr::select(seqnames, start, end)
anchor.up <- makeGRangesFromDataFrame(anchor.up.tb)
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
anchor.no.tb <- as_tibble(anchor.no) %>%
dplyr::mutate(center = (start + end)/2) %>%
dplyr::mutate(start = center-1, end = center+1) %>%
dplyr::select(seqnames, start, end)
anchor.no <- makeGRangesFromDataFrame(anchor.no.tb)
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
anchor.down.tb <- as_tibble(anchor.down) %>%
dplyr::mutate(center = (start + end)/2) %>%
dplyr::mutate(start = center-1, end = center+1) %>%
dplyr::select(seqnames, start, end)
anchor.down <- makeGRangesFromDataFrame(anchor.down.tb)
########################################################################
### Overlap
cscore.gr <- makeGRangesFromDataFrame(cscore[1:3])
overlap <- findOverlaps(anchor.up, cscore.gr)
## Problem of certain genes not getting overlap with cscore due to sparse calling?
batch1 <- bind_cols(data.table(anchor.up.tb)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed <- data.table(anchor.up.tb)[-queryHits(overlap)]
temp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%
dplyr::select(seqnames, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch2 <- bind_cols((missed)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
data.up <- bind_rows(batch1, batch2)
########################################################################
### Overlap
cscore.gr <- makeGRangesFromDataFrame(cscore[1:3])
overlap <- findOverlaps(anchor.no, cscore.gr)
## Problem of certain genes not getting overlap with cscore due to sparse calling?
batch1 <- bind_cols(data.table(anchor.no.tb)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed <- data.table(anchor.no.tb)[-queryHits(overlap)]
temp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%
dplyr::select(seqnames, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch2 <- bind_cols((missed)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
data.no <- bind_rows(batch1, batch2)
########################################################################
### Overlap
cscore.gr <- makeGRangesFromDataFrame(cscore[1:3])
overlap <- findOverlaps(anchor.down, cscore.gr)
## Problem of certain genes not getting overlap with cscore due to sparse calling?
batch1 <- bind_cols(data.table(anchor.down.tb)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed <- data.table(anchor.down.tb)[-queryHits(overlap)]
temp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%
dplyr::select(seqnames, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch2 <- bind_cols((missed)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
data.down <- bind_rows(batch1, batch2)
n.up <- nrow(data.up)
n.no <- nrow(data.no)
n.down <- nrow(data.down)
data <- tibble(loopDiff = c(rep(\UP\, n.up), rep(\NO\, n.no), rep(\DOWN\, n.down)),
changeType = c(data.up$changeType, data.no$changeType, data.down$changeType))
# VISUALIZE
data$changeType <- factor(data$changeType, levels = c(\A_weakening\, \AtoB\, \B_strengthening\,
\B_weakening\, \BtoA\, \A_strengthening\,
\Unchanged\))
ggplot(data, aes(x = loopDiff, fill = changeType)) +
geom_bar(position = \fill\) +
labs(title = \Stacked Bar Plot of Change Type by Diff\,
x = \Diff\,
y = \Count\) +
theme_bw() + scale_fill_manual(values = c(\skyblue\, \blue\, \darkblue\, \pink\, \red2\, \darkred\ ,\grey\))
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\\))\nanchor.up <- (extractAnchor(loop.up))\nanchor.up.tb <- as_tibble(anchor.up) %>%\n  dplyr::mutate(center = (start + end)/2) %>%\n  dplyr::mutate(start = center-1, end = center+1) %>%\n  dplyr::select(seqnames, start, end)\nanchor.up <- makeGRangesFromDataFrame(anchor.up.tb)\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\\))\nanchor.no <- (extractAnchor(loop.no))\nanchor.no.tb <- as_tibble(anchor.no) %>%\n  dplyr::mutate(center = (start + end)/2) %>%\n  dplyr::mutate(start = center-1, end = center+1) %>%\n  dplyr::select(seqnames, start, end)\nanchor.no <- makeGRangesFromDataFrame(anchor.no.tb)\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- (extractAnchor(loop.down))\nanchor.down.tb <- as_tibble(anchor.down) %>%\n  dplyr::mutate(center = (start + end)/2) %>%\n  dplyr::mutate(start = center-1, end = center+1) %>%\n  dplyr::select(seqnames, start, end)\nanchor.down <- makeGRangesFromDataFrame(anchor.down.tb)\n\n\n########################################################################\n### Overlap\ncscore.gr <- makeGRangesFromDataFrame(cscore[1:3])\noverlap <- findOverlaps(anchor.up, cscore.gr)\n\n## Problem of certain genes not getting overlap with cscore due to sparse calling?\nbatch1 <- bind_cols(data.table(anchor.up.tb)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\nmissed <- data.table(anchor.up.tb)[-queryHits(overlap)]\ntemp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%\n  dplyr::select(seqnames, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch2 <- bind_cols((missed)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\ndata.up <- bind_rows(batch1, batch2)\n\n########################################################################\n### Overlap\ncscore.gr <- makeGRangesFromDataFrame(cscore[1:3])\noverlap <- findOverlaps(anchor.no, cscore.gr)\n\n## Problem of certain genes not getting overlap with cscore due to sparse calling?\nbatch1 <- bind_cols(data.table(anchor.no.tb)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\nmissed <- data.table(anchor.no.tb)[-queryHits(overlap)]\ntemp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%\n  dplyr::select(seqnames, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch2 <- bind_cols((missed)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\ndata.no <- bind_rows(batch1, batch2)\n\n########################################################################\n### Overlap\ncscore.gr <- makeGRangesFromDataFrame(cscore[1:3])\noverlap <- findOverlaps(anchor.down, cscore.gr)\n\n## Problem of certain genes not getting overlap with cscore due to sparse calling?\nbatch1 <- bind_cols(data.table(anchor.down.tb)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\nmissed <- data.table(anchor.down.tb)[-queryHits(overlap)]\ntemp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%\n  dplyr::select(seqnames, start, end)\nmissed.gr <- makeGRangesFromDataFrame(temp)\noverlap <- findOverlaps(missed.gr, cscore.gr)\nbatch2 <- bind_cols((missed)[queryHits(overlap)],\n          data.table(cscore)[subjectHits(overlap)])\n\n\ndata.down <- bind_rows(batch1, batch2)\n\nn.up <- nrow(data.up)\nn.no <- nrow(data.no)\nn.down <- nrow(data.down)\n\ndata <- tibble(loopDiff = c(rep(\\UP\\, n.up), rep(\\NO\\, n.no), rep(\\DOWN\\, n.down)),\n              changeType = c(data.up$changeType, data.no$changeType, data.down$changeType))\n\n# VISUALIZE\ndata$changeType <- factor(data$changeType, levels = c(\\A_weakening\\, \\AtoB\\, \\B_strengthening\\,\n                                                                          \\B_weakening\\, \\BtoA\\, \\A_strengthening\\,\n                                                                          \\Unchanged\\))\nggplot(data, aes(x = loopDiff, fill = changeType)) +\n  geom_bar(position = \\fill\\) +\n  labs(title = \\Stacked Bar Plot of Change Type by Diff\\,\n       x = \\Diff\\,\n       y = \\Count\\) +\n  theme_bw() +  scale_fill_manual(values = c(\\skyblue\\, \\blue\\, \\darkblue\\, \\pink\\, \\red2\\, \\darkred\\ ,\\grey\\))\n\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_UP_diff0.2.bedpe\))
anchor.up <- (extractAnchor(loop.up))
anchor.up.tb <- as_tibble(anchor.up) %>%
dplyr::mutate(center = (start + end)/2) %>%
dplyr::mutate(start = center-1, end = center+1) %>%
dplyr::select(seqnames, start, end)
anchor.up <- makeGRangesFromDataFrame(anchor.up.tb)
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_NO_diff0.2.bedpe\))
anchor.no <- (extractAnchor(loop.no))
anchor.no.tb <- as_tibble(anchor.no) %>%
dplyr::mutate(center = (start + end)/2) %>%
dplyr::mutate(start = center-1, end = center+1) %>%
dplyr::select(seqnames, start, end)
anchor.no <- makeGRangesFromDataFrame(anchor.no.tb)
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_A485vsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- (extractAnchor(loop.down))
anchor.down.tb <- as_tibble(anchor.down) %>%
dplyr::mutate(center = (start + end)/2) %>%
dplyr::mutate(start = center-1, end = center+1) %>%
dplyr::select(seqnames, start, end)
anchor.down <- makeGRangesFromDataFrame(anchor.down.tb)
########################################################################
### Overlap
cscore.gr <- makeGRangesFromDataFrame(cscore[1:3])
overlap <- findOverlaps(anchor.up, cscore.gr)
## Problem of certain genes not getting overlap with cscore due to sparse calling?
batch1 <- bind_cols(data.table(anchor.up.tb)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed <- data.table(anchor.up.tb)[-queryHits(overlap)]
temp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%
dplyr::select(seqnames, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch2 <- bind_cols((missed)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
data.up <- bind_rows(batch1, batch2)
########################################################################
### Overlap
cscore.gr <- makeGRangesFromDataFrame(cscore[1:3])
overlap <- findOverlaps(anchor.no, cscore.gr)
## Problem of certain genes not getting overlap with cscore due to sparse calling?
batch1 <- bind_cols(data.table(anchor.no.tb)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed <- data.table(anchor.no.tb)[-queryHits(overlap)]
temp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%
dplyr::select(seqnames, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch2 <- bind_cols((missed)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
data.no <- bind_rows(batch1, batch2)
########################################################################
### Overlap
cscore.gr <- makeGRangesFromDataFrame(cscore[1:3])
overlap <- findOverlaps(anchor.down, cscore.gr)
## Problem of certain genes not getting overlap with cscore due to sparse calling?
batch1 <- bind_cols(data.table(anchor.down.tb)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
missed <- data.table(anchor.down.tb)[-queryHits(overlap)]
temp <- missed %>% dplyr::mutate(start = start - 10000, end = end - 10000) %>%
dplyr::select(seqnames, start, end)
missed.gr <- makeGRangesFromDataFrame(temp)
overlap <- findOverlaps(missed.gr, cscore.gr)
batch2 <- bind_cols((missed)[queryHits(overlap)],
data.table(cscore)[subjectHits(overlap)])
data.down <- bind_rows(batch1, batch2)
n.up <- nrow(data.up)
n.no <- nrow(data.no)
n.down <- nrow(data.down)
data <- tibble(loopDiff = c(rep(\UP\, n.up), rep(\NO\, n.no), rep(\DOWN\, n.down)),
changeType = c(data.up$changeType, data.no$changeType, data.down$changeType))
# VISUALIZE
data$changeType <- factor(data$changeType, levels = c(\A_weakening\, \AtoB\, \B_strengthening\,
\B_weakening\, \BtoA\, \A_strengthening\,
\Unchanged\))
ggplot(data, aes(x = loopDiff, fill = changeType)) +
geom_bar(position = \fill\) +
labs(title = \Stacked Bar Plot of Change Type by Diff\,
x = \Diff\,
y = \Count\) +
theme_bw() + scale_fill_manual(values = c(\skyblue\, \blue\, \darkblue\, \pink\, \red2\, \darkred\ ,\grey\))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Combining with RNA-seq
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
resultDir <- here(\../../result\)
data <- fread(here(resultDir, \chromHMM\, \A485_pe-pe_anchors\, \overlap_enrich_100_state.txt\))
colnames(data) <- c(\state\, \genome\, \pe-pe_A485_down\, \pe-pe_A485_no\, \pe-pe_A485_up\)
data <- data %>% dplyr::select(-genome)
data_matrix <- data %>%
column_to_rownames(var = \state\) %>%
as.matrix()
library(circlize)
col_fun <- colorRamp2(c(0, 1, 10),
c(\blue\, \white\, \red\))
Heatmap(
data_matrix,
name = \Value\,
show_row_names = TRUE,
show_column_names = TRUE,
cluster_columns = FALSE,
cluster_rows = FALSE,
col = col_fun
)
#######
data2 <- data %>% dplyr::select(c(1, 2, 4))
data2 <- data2 %>% dplyr::filter(`pe-pe_A485_down` > `pe-pe_A485_up`)
data_matrix <- data2 %>%
column_to_rownames(var = \state\) %>%
as.matrix()
library(circlize)
col_fun <- colorRamp2(c(0, 1, 10),
c(\blue\, \white\, \red\))
Heatmap(
data_matrix,
name = \Value\,
show_row_names = TRUE,
show_column_names = TRUE,
cluster_columns = FALSE,
cluster_rows = FALSE,
col = col_fun
)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
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```r
```r
resultDir <- here(\../../result\)
data <- fread(here(resultDir, \chromHMM\, \A485_pe-pe_anchors\, \overlap_enrich_100_state.txt\))
colnames(data) <- c(\state\, \genome\, \pe-pe_A485_down\, \pe-pe_A485_no\, \pe-pe_A485_up\)
data <- data %>% dplyr::select(-genome)
data_matrix <- data %>%
column_to_rownames(var = \state\) %>%
as.matrix()
library(circlize)
col_fun <- colorRamp2(c(0, 1, 10),
c(\blue\, \white\, \red\))
Heatmap(
data_matrix,
name = \Value\,
show_row_names = TRUE,
show_column_names = TRUE,
cluster_columns = FALSE,
cluster_rows = FALSE,
col = col_fun
)
#######
data2 <- data %>% dplyr::select(c(1, 2, 4))
data2 <- data2 %>% dplyr::filter(`pe-pe_A485_down` > `pe-pe_A485_up`)
data_matrix <- data2 %>%
column_to_rownames(var = \state\) %>%
as.matrix()
library(circlize)
col_fun <- colorRamp2(c(0, 1, 10),
c(\blue\, \white\, \red\))
Heatmap(
data_matrix,
name = \Value\,
show_row_names = TRUE,
show_column_names = TRUE,
cluster_columns = FALSE,
cluster_rows = FALSE,
col = col_fun
)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Combining with loop
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n### Getting loop ID\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nstrID <- temp$id\n\n############## REG\n## Filtering loops to plot\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\ntemp <- data %>% dplyr::filter(id %in% regID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/data_vault_2024summer_microC/hic\\\nwindowSize <- 1*1e6\n\ni = 1\n# for(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n  #cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.A485.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_regLoop_dTAG_\\, i, \\_10kb_figureVer\\))\n  \n  width <- panelSize(20)*mmToInch\n  height <- panelSize(10)*mmToInch\n  \n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = width, height = height)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_regLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  # svglite(paste0(fileName, \\.svg\\), width = 10, height = 5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n# }\n\n############## Str\n## Filtering loops to plot\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\ntemp <- data %>% dplyr::filter(id %in% strID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\\\nwindowSize <- 2*1e6\n\nfor(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_strLoop_dTAG_\\, i, \\_10kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_strLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n}\n\n############## dTAG called loops\n## Filtering loops to plot\ndata1 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_25kb.tsv\\))\ndata2 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_10kb.tsv\\))\ndata3 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_5kb.tsv\\))\ndata <- bind_rows(data1, data2, data3)\n\ntemp <- data %>% dplyr::arrange(desc(score))\n\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\\\nwindowSize <- 2*1e6\n\nfor(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_dTAGcalledLoop_dTAG_\\, i, \\_10kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_dTAGcalledLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n}\n\nchr = \\chr13\\\nstart = 96900000\nend = 98100000\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax))\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax))\n    print(cowplot::plot_grid(p1, p2, align = \\h\\))\n\n```\n\n<!-- rnb-source-end -->\n"} -->
### Getting loop ID
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\)) %>%
dplyr::mutate(res = V3 - V2,
id = paste(V1, res, V2, V5, sep = \_\))
regID <- temp$id
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure.bedpe\)) %>%
dplyr::mutate(res = V3 - V2,
id = paste(V1, res, V2, V5, sep = \_\))
strID <- temp$id
############## REG
## Filtering loops to plot
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- data %>% dplyr::filter(id %in% regID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))
temp <- temp %>% dplyr::select(seq(1, 6))
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loops <- importBedpe(temp)
# Visualization
hicDir <- \/Volumes/UKJIN_SSD/data_vault_2024summer_microC/hic\
windowSize <- 1*1e6
i = 1
# for(i in seq(1, 25)){
## Loading hic and plotting
chr <- as_tibble(loops[i])$seqnames1
center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2
start <- floor(center - 0.5*windowSize)
end <- floor(center + 0.5*windowSize)
cf.G1DMSO <- HicFile(path = here(hicDir, \G1.DMSO.Merged.hic\))
cf.G1dTAG <- HicFile(path = here(hicDir, \G1.dTAG.Merged.hic\))
#cf.G1dTAG <- HicFile(path = here(hicDir, \G1.A485.Merged.hic\))
res <- 10*1000
zmax <- 2.5
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_regLoop_dTAG_\, i, \_10kb_figureVer\))
width <- panelSize(20)*mmToInch
height <- panelSize(10)*mmToInch
png(paste0(fileName, \.png\), res = 600, units = \in\, width = width, height = height)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
svglite(paste0(fileName, \.svg\), width = width, height = height)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
res <- 25*1000
zmax <- 3
start <- floor(center - windowSize)
end <- floor(center + windowSize)
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_regLoop_dTAG_\, i, \_25kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
# svglite(paste0(fileName, \.svg\), width = 10, height = 5)
# print(cowplot::plot_grid(p1, p2, align = \h\))
# dev.off()
# }
############## Str
## Filtering loops to plot
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- data %>% dplyr::filter(id %in% strID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))
temp <- temp %>% dplyr::select(seq(1, 6))
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loops <- importBedpe(temp)
# Visualization
hicDir <- \/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\
windowSize <- 2*1e6
for(i in seq(1, 25)){
## Loading hic and plotting
chr <- as_tibble(loops[i])$seqnames1
center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2
start <- floor(center - 0.5*windowSize)
end <- floor(center + 0.5*windowSize)
cf.G1DMSO <- HicFile(path = here(hicDir, \G1.DMSO.Merged.hic\))
cf.G1dTAG <- HicFile(path = here(hicDir, \G1.dTAG.Merged.hic\))
res <- 10*1000
zmax <- 2.5
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_strLoop_dTAG_\, i, \_10kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
res <- 25*1000
zmax <- 3
start <- floor(center - windowSize)
end <- floor(center + windowSize)
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_strLoop_dTAG_\, i, \_25kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
}
############## dTAG called loops
## Filtering loops to plot
data1 <- fread(here(loopDir, \G1.dTAG.Merged_chromosight_25kb.tsv\))
data2 <- fread(here(loopDir, \G1.dTAG.Merged_chromosight_10kb.tsv\))
data3 <- fread(here(loopDir, \G1.dTAG.Merged_chromosight_5kb.tsv\))
data <- bind_rows(data1, data2, data3)
temp <- data %>% dplyr::arrange(desc(score))
temp <- temp %>% dplyr::select(seq(1, 6))
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loops <- importBedpe(temp)
# Visualization
hicDir <- \/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\
windowSize <- 2*1e6
for(i in seq(1, 25)){
## Loading hic and plotting
chr <- as_tibble(loops[i])$seqnames1
center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2
start <- floor(center - 0.5*windowSize)
end <- floor(center + 0.5*windowSize)
cf.G1DMSO <- HicFile(path = here(hicDir, \G1.DMSO.Merged.hic\))
cf.G1dTAG <- HicFile(path = here(hicDir, \G1.dTAG.Merged.hic\))
res <- 10*1000
zmax <- 2.5
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_dTAGcalledLoop_dTAG_\, i, \_10kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
res <- 25*1000
zmax <- 3
start <- floor(center - windowSize)
end <- floor(center + windowSize)
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_dTAGcalledLoop_dTAG_\, i, \_25kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
}
chr = \chr13\
start = 96900000
end = 98100000
res <- 10*1000
zmax <- 2.5
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax))
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax))
print(cowplot::plot_grid(p1, p2, align = \h\))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n### Getting loop ID\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nstrID <- temp$id\n\n############## REG\n## Filtering loops to plot\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\ntemp <- data %>% dplyr::filter(id %in% regID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/data_vault_2024summer_microC/hic\\\nwindowSize <- 1*1e6\n\ni = 1\n# for(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n  #cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.A485.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_regLoop_dTAG_\\, i, \\_10kb_figureVer\\))\n  \n  width <- panelSize(20)*mmToInch\n  height <- panelSize(10)*mmToInch\n  \n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = width, height = height)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_regLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  # svglite(paste0(fileName, \\.svg\\), width = 10, height = 5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n# }\n\n############## Str\n## Filtering loops to plot\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\ntemp <- data %>% dplyr::filter(id %in% strID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\\\nwindowSize <- 2*1e6\n\nfor(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_strLoop_dTAG_\\, i, \\_10kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_strLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n}\n\n############## dTAG called loops\n## Filtering loops to plot\ndata1 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_25kb.tsv\\))\ndata2 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_10kb.tsv\\))\ndata3 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_5kb.tsv\\))\ndata <- bind_rows(data1, data2, data3)\n\ntemp <- data %>% dplyr::arrange(desc(score))\n\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\\\nwindowSize <- 2*1e6\n\nfor(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_dTAGcalledLoop_dTAG_\\, i, \\_10kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_dTAGcalledLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n}\n\nchr = \\chr13\\\nstart = 96900000\nend = 98100000\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax))\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax))\n    print(cowplot::plot_grid(p1, p2, align = \\h\\))\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n### Getting loop ID\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nregID <- temp$id\n\ntemp <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure.bedpe\\)) %>%\n  dplyr::mutate(res = V3 - V2,\n                id = paste(V1, res, V2, V5, sep = \\_\\))\nstrID <- temp$id\n\n############## REG\n## Filtering loops to plot\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\ntemp <- data %>% dplyr::filter(id %in% regID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/data_vault_2024summer_microC/hic\\\nwindowSize <- 1*1e6\n\ni = 1\n# for(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n  #cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.A485.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_regLoop_dTAG_\\, i, \\_10kb_figureVer\\))\n  \n  width <- panelSize(20)*mmToInch\n  height <- panelSize(10)*mmToInch\n  \n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = width, height = height)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  svglite(paste0(fileName, \\.svg\\), width = width, height = height)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_regLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  # svglite(paste0(fileName, \\.svg\\), width = 10, height = 5)\n  # print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  # dev.off()\n# }\n\n############## Str\n## Filtering loops to plot\ndata <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\ntemp <- data %>% dplyr::filter(id %in% strID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\\\nwindowSize <- 2*1e6\n\nfor(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_strLoop_dTAG_\\, i, \\_10kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_strLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n}\n\n############## dTAG called loops\n## Filtering loops to plot\ndata1 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_25kb.tsv\\))\ndata2 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_10kb.tsv\\))\ndata3 <- fread(here(loopDir, \\G1.dTAG.Merged_chromosight_5kb.tsv\\))\ndata <- bind_rows(data1, data2, data3)\n\ntemp <- data %>% dplyr::arrange(desc(score))\n\ntemp <- temp %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\nloops <- importBedpe(temp)\n\n# Visualization\nhicDir <- \\/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\\\nwindowSize <- 2*1e6\n\nfor(i in seq(1, 25)){\n  ## Loading hic and plotting\n  chr <- as_tibble(loops[i])$seqnames1\n  center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2\n  start <- floor(center - 0.5*windowSize)\n  end <- floor(center + 0.5*windowSize)\n  \n  cf.G1DMSO <- HicFile(path = here(hicDir, \\G1.DMSO.Merged.hic\\))\n  cf.G1dTAG <- HicFile(path = here(hicDir, \\G1.dTAG.Merged.hic\\))\n\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_dTAGcalledLoop_dTAG_\\, i, \\_10kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n  \n  \n  res <- 25*1000\n  zmax <- 3\n  start <- floor(center - windowSize)\n  end <- floor(center + windowSize)\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)\n  \n  fileName <- here(figDir, paste0(\\visuzliation_dTAGcalledLoop_dTAG_\\, i, \\_25kb\\))\n  png(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 10, height = 5)\n  print(cowplot::plot_grid(p1, p2, align = \\h\\))\n  dev.off()\n}\n\nchr = \\chr13\\\nstart = 96900000\nend = 98100000\n  res <- 10*1000\n  zmax <- 2.5\n  hic1 <- import(cf.G1DMSO, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  hic2 <- import(cf.G1dTAG, focus = paste0(chr, \\:\\, start, \\-\\, end), resolution = res)\n  p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax))\n  p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax))\n    print(cowplot::plot_grid(p1, p2, align = \\h\\))\n\n```\n```"} -->
```r
```r
### Getting loop ID
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\)) %>%
dplyr::mutate(res = V3 - V2,
id = paste(V1, res, V2, V5, sep = \_\))
regID <- temp$id
temp <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure.bedpe\)) %>%
dplyr::mutate(res = V3 - V2,
id = paste(V1, res, V2, V5, sep = \_\))
strID <- temp$id
############## REG
## Filtering loops to plot
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- data %>% dplyr::filter(id %in% regID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))
temp <- temp %>% dplyr::select(seq(1, 6))
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loops <- importBedpe(temp)
# Visualization
hicDir <- \/Volumes/UKJIN_SSD/data_vault_2024summer_microC/hic\
windowSize <- 1*1e6
i = 1
# for(i in seq(1, 25)){
## Loading hic and plotting
chr <- as_tibble(loops[i])$seqnames1
center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2
start <- floor(center - 0.5*windowSize)
end <- floor(center + 0.5*windowSize)
cf.G1DMSO <- HicFile(path = here(hicDir, \G1.DMSO.Merged.hic\))
cf.G1dTAG <- HicFile(path = here(hicDir, \G1.dTAG.Merged.hic\))
#cf.G1dTAG <- HicFile(path = here(hicDir, \G1.A485.Merged.hic\))
res <- 10*1000
zmax <- 2.5
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_regLoop_dTAG_\, i, \_10kb_figureVer\))
width <- panelSize(20)*mmToInch
height <- panelSize(10)*mmToInch
png(paste0(fileName, \.png\), res = 600, units = \in\, width = width, height = height)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
svglite(paste0(fileName, \.svg\), width = width, height = height)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
res <- 25*1000
zmax <- 3
start <- floor(center - windowSize)
end <- floor(center + windowSize)
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_regLoop_dTAG_\, i, \_25kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
# svglite(paste0(fileName, \.svg\), width = 10, height = 5)
# print(cowplot::plot_grid(p1, p2, align = \h\))
# dev.off()
# }
############## Str
## Filtering loops to plot
data <- fread(here(consensusDir, \chromo_cons_score.tsv\))
temp <- data %>% dplyr::filter(id %in% strID, dTAG > 0.5) %>% dplyr::arrange(desc(dTAG))
temp <- temp %>% dplyr::select(seq(1, 6))
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loops <- importBedpe(temp)
# Visualization
hicDir <- \/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\
windowSize <- 2*1e6
for(i in seq(1, 25)){
## Loading hic and plotting
chr <- as_tibble(loops[i])$seqnames1
center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2
start <- floor(center - 0.5*windowSize)
end <- floor(center + 0.5*windowSize)
cf.G1DMSO <- HicFile(path = here(hicDir, \G1.DMSO.Merged.hic\))
cf.G1dTAG <- HicFile(path = here(hicDir, \G1.dTAG.Merged.hic\))
res <- 10*1000
zmax <- 2.5
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_strLoop_dTAG_\, i, \_10kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
res <- 25*1000
zmax <- 3
start <- floor(center - windowSize)
end <- floor(center + windowSize)
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_strLoop_dTAG_\, i, \_25kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
}
############## dTAG called loops
## Filtering loops to plot
data1 <- fread(here(loopDir, \G1.dTAG.Merged_chromosight_25kb.tsv\))
data2 <- fread(here(loopDir, \G1.dTAG.Merged_chromosight_10kb.tsv\))
data3 <- fread(here(loopDir, \G1.dTAG.Merged_chromosight_5kb.tsv\))
data <- bind_rows(data1, data2, data3)
temp <- data %>% dplyr::arrange(desc(score))
temp <- temp %>% dplyr::select(seq(1, 6))
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
loops <- importBedpe(temp)
# Visualization
hicDir <- \/Volumes/UKJIN_SSD/Genomics_03_Analysis_Working/data_vault_2024summer_microC/hic\
windowSize <- 2*1e6
for(i in seq(1, 25)){
## Loading hic and plotting
chr <- as_tibble(loops[i])$seqnames1
center <- (as_tibble(loops[i])$start1 + as_tibble(loops[i])$end2)/2
start <- floor(center - 0.5*windowSize)
end <- floor(center + 0.5*windowSize)
cf.G1DMSO <- HicFile(path = here(hicDir, \G1.DMSO.Merged.hic\))
cf.G1dTAG <- HicFile(path = here(hicDir, \G1.dTAG.Merged.hic\))
res <- 10*1000
zmax <- 2.5
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_dTAGcalledLoop_dTAG_\, i, \_10kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
res <- 25*1000
zmax <- 3
start <- floor(center - windowSize)
end <- floor(center + windowSize)
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax), loop = loops)
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax), loop = loops)
fileName <- here(figDir, paste0(\visuzliation_dTAGcalledLoop_dTAG_\, i, \_25kb\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 10, height = 5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
}
chr = \chr13\
start = 96900000
end = 98100000
res <- 10*1000
zmax <- 2.5
hic1 <- import(cf.G1DMSO, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
hic2 <- import(cf.G1dTAG, focus = paste0(chr, \:\, start, \-\, end), resolution = res)
p1 <- plotMatrix(hic1, dpi = 1000, limits = c(0, zmax))
p2 <- plotMatrix(hic2, dpi = 1000, limits = c(0, zmax))
print(cowplot::plot_grid(p1, p2, align = \h\))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.30] ChromHMM
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nlibrary(circlize)\n\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ntemp <- data %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\ncons.loop <- importBedpe(temp)\n\n# Checking DMSO\nloop.25kb <- importBedpe(fread(here(loopDir, paste0(\\G1.DMSO.Merged_chromosight_\\, 25, \\kb.bedpe\\))))\nloop.10kb <- importBedpe(fread(here(loopDir, paste0(\\G1.DMSO.Merged_chromosight_\\, 10, \\kb.bedpe\\))))\nloop.5kb <- importBedpe(fread(here(loopDir, paste0(\\G1.DMSO.Merged_chromosight_\\, 5, \\kb.bedpe\\))))\n\n\noverlap.25kb <- findOverlaps(cons.loop, loop.25kb)\noverlap.10kb <- findOverlaps(cons.loop, loop.10kb)\noverlap.5kb <- findOverlaps(cons.loop, loop.5kb)\n\nindex <- sort(unique(c(queryHits(overlap.25kb),\n         queryHits(overlap.10kb),\n         queryHits(overlap.5kb))))\ndata$calledByDMSO <- 0\ndata$calledByDMSO[index] <- 1\n\n# Checking dTAG\nloop.25kb <- importBedpe(fread(here(loopDir, paste0(\\G1.dTAG.Merged_chromosight_\\, 25, \\kb.bedpe\\))))\nloop.10kb <- importBedpe(fread(here(loopDir, paste0(\\G1.dTAG.Merged_chromosight_\\, 10, \\kb.bedpe\\))))\nloop.5kb <- importBedpe(fread(here(loopDir, paste0(\\G1.dTAG.Merged_chromosight_\\, 5, \\kb.bedpe\\))))\n\n\noverlap.25kb <- findOverlaps(cons.loop, loop.25kb)\noverlap.10kb <- findOverlaps(cons.loop, loop.10kb)\noverlap.5kb <- findOverlaps(cons.loop, loop.5kb)\n\nindex <- sort(unique(c(queryHits(overlap.25kb),\n         queryHits(overlap.10kb),\n         queryHits(overlap.5kb))))\ndata$calledBydTAG <- 0\ndata$calledBydTAG[index] <- 1\n\n# Checking A485\nloop.25kb <- importBedpe(fread(here(loopDir, paste0(\\G1.A485.Merged_chromosight_\\, 25, \\kb.bedpe\\))))\nloop.10kb <- importBedpe(fread(here(loopDir, paste0(\\G1.A485.Merged_chromosight_\\, 10, \\kb.bedpe\\))))\nloop.5kb <- importBedpe(fread(here(loopDir, paste0(\\G1.A485.Merged_chromosight_\\, 5, \\kb.bedpe\\))))\n\n\noverlap.25kb <- findOverlaps(cons.loop, loop.25kb)\noverlap.10kb <- findOverlaps(cons.loop, loop.10kb)\noverlap.5kb <- findOverlaps(cons.loop, loop.5kb)\n\nindex <- sort(unique(c(queryHits(overlap.25kb),\n         queryHits(overlap.10kb),\n         queryHits(overlap.5kb))))\ndata$calledByA485 <- 0\ndata$calledByA485[index] <- 1\n\n\n# Heatmap (checking whether certain peak is called by specific sample)\ndataToPlot <- data %>% dplyr::select(id, calledByDMSO, calledBydTAG, calledByA485)\ndataToPlot <- dataToPlot %>% dplyr::mutate(flag = 4*calledByDMSO + 2*calledBydTAG + calledByA485) %>%\n  dplyr::arrange(desc(flag))\n\nrow_groups <- dataToPlot$flag\ndata_matrix <- dataToPlot %>% column_to_rownames(var = \\id\\) %>% dplyr::select(-flag) %>% as.matrix()\ncol_fun <- colorRamp2(c(0, 1), \n                      c(\\white\\, \\green\\))\nh1 <- Heatmap(data_matrix, name = \\calledBy\\,\n        cluster_columns = FALSE,\n        cluster_rows = FALSE,\n        show_row_dend = FALSE,\n        border = TRUE,\n        show_row_names = FALSE,\n        col = col_fun,\n        row_split = row_groups)\n\n\n# Prepare data for the second heatmap\ndataToPlot2 <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%\n  arrange(match(id, dataToPlot$id))\n\ndata_matrix2 <- dataToPlot2 %>% column_to_rownames(var = \\id\\) %>% as.matrix()\n\n\ncol_fun2 <- colorRamp2(c(-0.5, 0, 1), c(\\blue\\, \\white\\, \\red\\))\n\nh2 <- Heatmap(data_matrix2, name = \\score\\,\n              cluster_columns = FALSE,\n              cluster_rows = TRUE,\n              show_row_dend = FALSE,\n              border = TRUE,\n              show_row_names = FALSE,\n              col = col_fun2,\n              row_split = row_groups)\n\n\nfileName <- here(figDir, paste0(\\heatmap_checkingSampleCalledLoops\\))\npng(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 3, height = 10)\nprint(h1 + h2)\ndev.off()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
library(circlize)
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
temp <- data %>% dplyr::select(seq(1, 6))
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
cons.loop <- importBedpe(temp)
# Checking DMSO
loop.25kb <- importBedpe(fread(here(loopDir, paste0(\G1.DMSO.Merged_chromosight_\, 25, \kb.bedpe\))))
loop.10kb <- importBedpe(fread(here(loopDir, paste0(\G1.DMSO.Merged_chromosight_\, 10, \kb.bedpe\))))
loop.5kb <- importBedpe(fread(here(loopDir, paste0(\G1.DMSO.Merged_chromosight_\, 5, \kb.bedpe\))))
overlap.25kb <- findOverlaps(cons.loop, loop.25kb)
overlap.10kb <- findOverlaps(cons.loop, loop.10kb)
overlap.5kb <- findOverlaps(cons.loop, loop.5kb)
index <- sort(unique(c(queryHits(overlap.25kb),
queryHits(overlap.10kb),
queryHits(overlap.5kb))))
data$calledByDMSO <- 0
data$calledByDMSO[index] <- 1
# Checking dTAG
loop.25kb <- importBedpe(fread(here(loopDir, paste0(\G1.dTAG.Merged_chromosight_\, 25, \kb.bedpe\))))
loop.10kb <- importBedpe(fread(here(loopDir, paste0(\G1.dTAG.Merged_chromosight_\, 10, \kb.bedpe\))))
loop.5kb <- importBedpe(fread(here(loopDir, paste0(\G1.dTAG.Merged_chromosight_\, 5, \kb.bedpe\))))
overlap.25kb <- findOverlaps(cons.loop, loop.25kb)
overlap.10kb <- findOverlaps(cons.loop, loop.10kb)
overlap.5kb <- findOverlaps(cons.loop, loop.5kb)
index <- sort(unique(c(queryHits(overlap.25kb),
queryHits(overlap.10kb),
queryHits(overlap.5kb))))
data$calledBydTAG <- 0
data$calledBydTAG[index] <- 1
# Checking A485
loop.25kb <- importBedpe(fread(here(loopDir, paste0(\G1.A485.Merged_chromosight_\, 25, \kb.bedpe\))))
loop.10kb <- importBedpe(fread(here(loopDir, paste0(\G1.A485.Merged_chromosight_\, 10, \kb.bedpe\))))
loop.5kb <- importBedpe(fread(here(loopDir, paste0(\G1.A485.Merged_chromosight_\, 5, \kb.bedpe\))))
overlap.25kb <- findOverlaps(cons.loop, loop.25kb)
overlap.10kb <- findOverlaps(cons.loop, loop.10kb)
overlap.5kb <- findOverlaps(cons.loop, loop.5kb)
index <- sort(unique(c(queryHits(overlap.25kb),
queryHits(overlap.10kb),
queryHits(overlap.5kb))))
data$calledByA485 <- 0
data$calledByA485[index] <- 1
# Heatmap (checking whether certain peak is called by specific sample)
dataToPlot <- data %>% dplyr::select(id, calledByDMSO, calledBydTAG, calledByA485)
dataToPlot <- dataToPlot %>% dplyr::mutate(flag = 4*calledByDMSO + 2*calledBydTAG + calledByA485) %>%
dplyr::arrange(desc(flag))
row_groups <- dataToPlot$flag
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\) %>% dplyr::select(-flag) %>% as.matrix()
col_fun <- colorRamp2(c(0, 1),
c(\white\, \green\))
h1 <- Heatmap(data_matrix, name = \calledBy\,
cluster_columns = FALSE,
cluster_rows = FALSE,
show_row_dend = FALSE,
border = TRUE,
show_row_names = FALSE,
col = col_fun,
row_split = row_groups)
# Prepare data for the second heatmap
dataToPlot2 <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%
arrange(match(id, dataToPlot$id))
data_matrix2 <- dataToPlot2 %>% column_to_rownames(var = \id\) %>% as.matrix()
col_fun2 <- colorRamp2(c(-0.5, 0, 1), c(\blue\, \white\, \red\))
h2 <- Heatmap(data_matrix2, name = \score\,
cluster_columns = FALSE,
cluster_rows = TRUE,
show_row_dend = FALSE,
border = TRUE,
show_row_names = FALSE,
col = col_fun2,
row_split = row_groups)
fileName <- here(figDir, paste0(\heatmap_checkingSampleCalledLoops\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 3, height = 10)
print(h1 + h2)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nlibrary(circlize)\n\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\ntemp <- data %>% dplyr::select(seq(1, 6))\ncolnames(temp) <- c(\\V1\\, \\V2\\, \\V3\\, \\V4\\, \\V5\\, \\V6\\)\ncons.loop <- importBedpe(temp)\n\n# Checking DMSO\nloop.25kb <- importBedpe(fread(here(loopDir, paste0(\\G1.DMSO.Merged_chromosight_\\, 25, \\kb.bedpe\\))))\nloop.10kb <- importBedpe(fread(here(loopDir, paste0(\\G1.DMSO.Merged_chromosight_\\, 10, \\kb.bedpe\\))))\nloop.5kb <- importBedpe(fread(here(loopDir, paste0(\\G1.DMSO.Merged_chromosight_\\, 5, \\kb.bedpe\\))))\n\n\noverlap.25kb <- findOverlaps(cons.loop, loop.25kb)\noverlap.10kb <- findOverlaps(cons.loop, loop.10kb)\noverlap.5kb <- findOverlaps(cons.loop, loop.5kb)\n\nindex <- sort(unique(c(queryHits(overlap.25kb),\n         queryHits(overlap.10kb),\n         queryHits(overlap.5kb))))\ndata$calledByDMSO <- 0\ndata$calledByDMSO[index] <- 1\n\n# Checking dTAG\nloop.25kb <- importBedpe(fread(here(loopDir, paste0(\\G1.dTAG.Merged_chromosight_\\, 25, \\kb.bedpe\\))))\nloop.10kb <- importBedpe(fread(here(loopDir, paste0(\\G1.dTAG.Merged_chromosight_\\, 10, \\kb.bedpe\\))))\nloop.5kb <- importBedpe(fread(here(loopDir, paste0(\\G1.dTAG.Merged_chromosight_\\, 5, \\kb.bedpe\\))))\n\n\noverlap.25kb <- findOverlaps(cons.loop, loop.25kb)\noverlap.10kb <- findOverlaps(cons.loop, loop.10kb)\noverlap.5kb <- findOverlaps(cons.loop, loop.5kb)\n\nindex <- sort(unique(c(queryHits(overlap.25kb),\n         queryHits(overlap.10kb),\n         queryHits(overlap.5kb))))\ndata$calledBydTAG <- 0\ndata$calledBydTAG[index] <- 1\n\n# Checking A485\nloop.25kb <- importBedpe(fread(here(loopDir, paste0(\\G1.A485.Merged_chromosight_\\, 25, \\kb.bedpe\\))))\nloop.10kb <- importBedpe(fread(here(loopDir, paste0(\\G1.A485.Merged_chromosight_\\, 10, \\kb.bedpe\\))))\nloop.5kb <- importBedpe(fread(here(loopDir, paste0(\\G1.A485.Merged_chromosight_\\, 5, \\kb.bedpe\\))))\n\n\noverlap.25kb <- findOverlaps(cons.loop, loop.25kb)\noverlap.10kb <- findOverlaps(cons.loop, loop.10kb)\noverlap.5kb <- findOverlaps(cons.loop, loop.5kb)\n\nindex <- sort(unique(c(queryHits(overlap.25kb),\n         queryHits(overlap.10kb),\n         queryHits(overlap.5kb))))\ndata$calledByA485 <- 0\ndata$calledByA485[index] <- 1\n\n\n# Heatmap (checking whether certain peak is called by specific sample)\ndataToPlot <- data %>% dplyr::select(id, calledByDMSO, calledBydTAG, calledByA485)\ndataToPlot <- dataToPlot %>% dplyr::mutate(flag = 4*calledByDMSO + 2*calledBydTAG + calledByA485) %>%\n  dplyr::arrange(desc(flag))\n\nrow_groups <- dataToPlot$flag\ndata_matrix <- dataToPlot %>% column_to_rownames(var = \\id\\) %>% dplyr::select(-flag) %>% as.matrix()\ncol_fun <- colorRamp2(c(0, 1), \n                      c(\\white\\, \\green\\))\nh1 <- Heatmap(data_matrix, name = \\calledBy\\,\n        cluster_columns = FALSE,\n        cluster_rows = FALSE,\n        show_row_dend = FALSE,\n        border = TRUE,\n        show_row_names = FALSE,\n        col = col_fun,\n        row_split = row_groups)\n\n\n# Prepare data for the second heatmap\ndataToPlot2 <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%\n  arrange(match(id, dataToPlot$id))\n\ndata_matrix2 <- dataToPlot2 %>% column_to_rownames(var = \\id\\) %>% as.matrix()\n\n\ncol_fun2 <- colorRamp2(c(-0.5, 0, 1), c(\\blue\\, \\white\\, \\red\\))\n\nh2 <- Heatmap(data_matrix2, name = \\score\\,\n              cluster_columns = FALSE,\n              cluster_rows = TRUE,\n              show_row_dend = FALSE,\n              border = TRUE,\n              show_row_names = FALSE,\n              col = col_fun2,\n              row_split = row_groups)\n\n\nfileName <- here(figDir, paste0(\\heatmap_checkingSampleCalledLoops\\))\npng(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 3, height = 10)\nprint(h1 + h2)\ndev.off()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
library(circlize)
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
temp <- data %>% dplyr::select(seq(1, 6))
colnames(temp) <- c(\V1\, \V2\, \V3\, \V4\, \V5\, \V6\)
cons.loop <- importBedpe(temp)
# Checking DMSO
loop.25kb <- importBedpe(fread(here(loopDir, paste0(\G1.DMSO.Merged_chromosight_\, 25, \kb.bedpe\))))
loop.10kb <- importBedpe(fread(here(loopDir, paste0(\G1.DMSO.Merged_chromosight_\, 10, \kb.bedpe\))))
loop.5kb <- importBedpe(fread(here(loopDir, paste0(\G1.DMSO.Merged_chromosight_\, 5, \kb.bedpe\))))
overlap.25kb <- findOverlaps(cons.loop, loop.25kb)
overlap.10kb <- findOverlaps(cons.loop, loop.10kb)
overlap.5kb <- findOverlaps(cons.loop, loop.5kb)
index <- sort(unique(c(queryHits(overlap.25kb),
queryHits(overlap.10kb),
queryHits(overlap.5kb))))
data$calledByDMSO <- 0
data$calledByDMSO[index] <- 1
# Checking dTAG
loop.25kb <- importBedpe(fread(here(loopDir, paste0(\G1.dTAG.Merged_chromosight_\, 25, \kb.bedpe\))))
loop.10kb <- importBedpe(fread(here(loopDir, paste0(\G1.dTAG.Merged_chromosight_\, 10, \kb.bedpe\))))
loop.5kb <- importBedpe(fread(here(loopDir, paste0(\G1.dTAG.Merged_chromosight_\, 5, \kb.bedpe\))))
overlap.25kb <- findOverlaps(cons.loop, loop.25kb)
overlap.10kb <- findOverlaps(cons.loop, loop.10kb)
overlap.5kb <- findOverlaps(cons.loop, loop.5kb)
index <- sort(unique(c(queryHits(overlap.25kb),
queryHits(overlap.10kb),
queryHits(overlap.5kb))))
data$calledBydTAG <- 0
data$calledBydTAG[index] <- 1
# Checking A485
loop.25kb <- importBedpe(fread(here(loopDir, paste0(\G1.A485.Merged_chromosight_\, 25, \kb.bedpe\))))
loop.10kb <- importBedpe(fread(here(loopDir, paste0(\G1.A485.Merged_chromosight_\, 10, \kb.bedpe\))))
loop.5kb <- importBedpe(fread(here(loopDir, paste0(\G1.A485.Merged_chromosight_\, 5, \kb.bedpe\))))
overlap.25kb <- findOverlaps(cons.loop, loop.25kb)
overlap.10kb <- findOverlaps(cons.loop, loop.10kb)
overlap.5kb <- findOverlaps(cons.loop, loop.5kb)
index <- sort(unique(c(queryHits(overlap.25kb),
queryHits(overlap.10kb),
queryHits(overlap.5kb))))
data$calledByA485 <- 0
data$calledByA485[index] <- 1
# Heatmap (checking whether certain peak is called by specific sample)
dataToPlot <- data %>% dplyr::select(id, calledByDMSO, calledBydTAG, calledByA485)
dataToPlot <- dataToPlot %>% dplyr::mutate(flag = 4*calledByDMSO + 2*calledBydTAG + calledByA485) %>%
dplyr::arrange(desc(flag))
row_groups <- dataToPlot$flag
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\) %>% dplyr::select(-flag) %>% as.matrix()
col_fun <- colorRamp2(c(0, 1),
c(\white\, \green\))
h1 <- Heatmap(data_matrix, name = \calledBy\,
cluster_columns = FALSE,
cluster_rows = FALSE,
show_row_dend = FALSE,
border = TRUE,
show_row_names = FALSE,
col = col_fun,
row_split = row_groups)
# Prepare data for the second heatmap
dataToPlot2 <- data %>% dplyr::select(id, DMSO, dTAG, A485) %>%
arrange(match(id, dataToPlot$id))
data_matrix2 <- dataToPlot2 %>% column_to_rownames(var = \id\) %>% as.matrix()
col_fun2 <- colorRamp2(c(-0.5, 0, 1), c(\blue\, \white\, \red\))
h2 <- Heatmap(data_matrix2, name = \score\,
cluster_columns = FALSE,
cluster_rows = TRUE,
show_row_dend = FALSE,
border = TRUE,
show_row_names = FALSE,
col = col_fun2,
row_split = row_groups)
fileName <- here(figDir, paste0(\heatmap_checkingSampleCalledLoops\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 3, height = 10)
print(h1 + h2)
dev.off()
```
```
<!-- rnb-source-end -->
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## [2.31] Making Visualization Easy
https://bioconductor.org/books/devel/OHCA/pages/visualization.html
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# Heatmap
library(circlize)
# Heatmap 2
set.seed(123)
diffCutoff <- 0.2
dataToPlot <- data %>% dplyr::mutate(isdiff_dTAG_DMSO =
case_when(diff_dTAG_DMSO >= diffCutoff ~ 1,
abs(diff_dTAG_DMSO) < diffCutoff ~ 0,
diff_dTAG_DMSO <= -diffCutoff ~ -1,
TRUE ~ NA),
isdiff_A485_DMSO =
case_when(diff_A485_DMSO >= diffCutoff ~ 1,
abs(diff_A485_DMSO) < diffCutoff ~ 0,
diff_A485_DMSO <= -diffCutoff ~ -1,
TRUE ~ NA)) %>%
dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \grey\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 9,
show_row_dend = FALSE,
border = FALSE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_checkingDeltaAcrossCondition\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 10)
print(h1)
dev.off()
set.seed(123)
hm_drawn <- draw(h1)
row_clusters <- row_order(hm_drawn)
loop.cluster1 <- temp[row_clusters[[1]]]
loop.cluster2 <- temp[row_clusters[[2]]]
loop.cluster3 <- temp[row_clusters[[3]]]
loop.cluster4 <- temp[row_clusters[[4]]]
loop.cluster5 <- temp[row_clusters[[5]]]
loop.cluster6 <- temp[row_clusters[[6]]]
loop.cluster7 <- temp[row_clusters[[7]]]
loop.cluster8 <- temp[row_clusters[[8]]]
loop.cluster9 <- temp[row_clusters[[9]]]
<!-- rnb-output-end -->
<!-- rnb-output-begin 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-->
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```r
```r
# Heatmap
library(circlize)
# Heatmap 2
set.seed(123)
diffCutoff <- 0.2
dataToPlot <- data %>% dplyr::mutate(isdiff_dTAG_DMSO =
case_when(diff_dTAG_DMSO >= diffCutoff ~ 1,
abs(diff_dTAG_DMSO) < diffCutoff ~ 0,
diff_dTAG_DMSO <= -diffCutoff ~ -1,
TRUE ~ NA),
isdiff_A485_DMSO =
case_when(diff_A485_DMSO >= diffCutoff ~ 1,
abs(diff_A485_DMSO) < diffCutoff ~ 0,
diff_A485_DMSO <= -diffCutoff ~ -1,
TRUE ~ NA)) %>%
dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \grey\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 9,
show_row_dend = FALSE,
border = FALSE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_checkingDeltaAcrossCondition\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 10)
print(h1)
dev.off()
set.seed(123)
hm_drawn <- draw(h1)
row_clusters <- row_order(hm_drawn)
loop.cluster1 <- temp[row_clusters[[1]]]
loop.cluster2 <- temp[row_clusters[[2]]]
loop.cluster3 <- temp[row_clusters[[3]]]
loop.cluster4 <- temp[row_clusters[[4]]]
loop.cluster5 <- temp[row_clusters[[5]]]
loop.cluster6 <- temp[row_clusters[[6]]]
loop.cluster7 <- temp[row_clusters[[7]]]
loop.cluster8 <- temp[row_clusters[[8]]]
loop.cluster9 <- temp[row_clusters[[9]]]
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## [2.32] Heatmap of loops
Strategy: check overlap of the union loops to the loops called at each condition
#### Check sample specific called loops
<!-- rnb-text-end -->
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# Heatmap
library(circlize)
# Heatmap 2
set.seed(123)
diffCutoff <- 0.2
dataToPlot <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>%
dplyr::mutate(isdiff_dTAG_DMSO =
case_when(diff_dTAG_DMSO >= diffCutoff ~ 1,
abs(diff_dTAG_DMSO) < diffCutoff ~ 0,
diff_dTAG_DMSO <= -diffCutoff ~ -1,
TRUE ~ NA),
isdiff_A485_DMSO =
case_when(diff_A485_DMSO >= diffCutoff ~ 1,
abs(diff_A485_DMSO) < diffCutoff ~ 0,
diff_A485_DMSO <= -diffCutoff ~ -1,
TRUE ~ NA)) %>%
dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \grey\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 8,
show_row_dend = FALSE,
border = FALSE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_checkingDeltaAcrossCondition_reg\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 10)
print(h1)
dev.off()
set.seed(123)
hm_drawn <- draw(h1)
row_clusters <- row_order(hm_drawn)
loop.cluster1 <- temp[row_clusters[[1]]]
loop.cluster2 <- temp[row_clusters[[2]]]
loop.cluster3 <- temp[row_clusters[[3]]]
loop.cluster4 <- temp[row_clusters[[4]]]
loop.cluster5 <- temp[row_clusters[[5]]]
loop.cluster6 <- temp[row_clusters[[6]]]
loop.cluster7 <- temp[row_clusters[[7]]]
loop.cluster8 <- temp[row_clusters[[8]]]
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<!-- rnb-output-begin 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```r
```r
# Heatmap
library(circlize)
# Heatmap 2
set.seed(123)
diffCutoff <- 0.2
dataToPlot <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>%
dplyr::mutate(isdiff_dTAG_DMSO =
case_when(diff_dTAG_DMSO >= diffCutoff ~ 1,
abs(diff_dTAG_DMSO) < diffCutoff ~ 0,
diff_dTAG_DMSO <= -diffCutoff ~ -1,
TRUE ~ NA),
isdiff_A485_DMSO =
case_when(diff_A485_DMSO >= diffCutoff ~ 1,
abs(diff_A485_DMSO) < diffCutoff ~ 0,
diff_A485_DMSO <= -diffCutoff ~ -1,
TRUE ~ NA)) %>%
dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \grey\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 8,
show_row_dend = FALSE,
border = FALSE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_checkingDeltaAcrossCondition_reg\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 10)
print(h1)
dev.off()
set.seed(123)
hm_drawn <- draw(h1)
row_clusters <- row_order(hm_drawn)
loop.cluster1 <- temp[row_clusters[[1]]]
loop.cluster2 <- temp[row_clusters[[2]]]
loop.cluster3 <- temp[row_clusters[[3]]]
loop.cluster4 <- temp[row_clusters[[4]]]
loop.cluster5 <- temp[row_clusters[[5]]]
loop.cluster6 <- temp[row_clusters[[6]]]
loop.cluster7 <- temp[row_clusters[[7]]]
loop.cluster8 <- temp[row_clusters[[8]]]
```
```
<!-- rnb-source-end -->
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#### Checking updown
##### All loops
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library(circlize)
set.seed(123)
dataToPlot <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>%
dplyr::select(id, diff_dTAG_DMSO, diff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \white\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 4,
show_row_dend = FALSE,
border = TRUE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_diffScore_reg_k4\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 6)
print(h1)
dev.off()
hm_drawn <- draw(h1)
row_clusters <- row_order(hm_drawn)
loop.cluster1 <- temp[row_clusters[[1]]]
loop.cluster2 <- temp[row_clusters[[2]]]
loop.cluster3 <- temp[row_clusters[[3]]]
loop.cluster4 <- temp[row_clusters[[4]]]
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```r
```r
library(circlize)
set.seed(123)
dataToPlot <- data %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>%
dplyr::select(id, diff_dTAG_DMSO, diff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \white\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 4,
show_row_dend = FALSE,
border = TRUE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_diffScore_reg_k4\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 6)
print(h1)
dev.off()
hm_drawn <- draw(h1)
row_clusters <- row_order(hm_drawn)
loop.cluster1 <- temp[row_clusters[[1]]]
loop.cluster2 <- temp[row_clusters[[2]]]
loop.cluster3 <- temp[row_clusters[[3]]]
loop.cluster4 <- temp[row_clusters[[4]]]
```
```
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##### Reg loops
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n# Heatmap\nlibrary(circlize)\n\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n# Heatmap 2\nset.seed(123)\n\n# Import obs/exp scores and merge to the dataset\nminValue <- -4\ndiffCutoff <- 0.5\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n    dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                  log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                  log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))\n\n\n# Merge dataset\ndataToPlot <- data %>% dplyr::left_join(obsexp, by = c(\\id\\)) %>%\n  dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO,\n                isdiff_dTAG_DMSO = case_when(log_obsexp_diff_dTAG_DMSO >= diffCutoff ~ 1,\n                                             abs(log_obsexp_diff_dTAG_DMSO) < diffCutoff ~ 0,\n                                             log_obsexp_diff_dTAG_DMSO <= diffCutoff ~ -1,\n                                             TRUE ~ NA ),\n                isdiff_A485_DMSO = case_when(log_obsexp_diff_A485_DMSO >= diffCutoff ~ 1,\n                                             abs(log_obsexp_diff_A485_DMSO) < diffCutoff ~ 0,\n                                             log_obsexp_diff_A485_DMSO <= diffCutoff ~ -1,\n                                             TRUE ~ NA))%>%\n  dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)\n\n\n\ndata_matrix <- dataToPlot %>% column_to_rownames(var = \\id\\)%>% as.matrix()\ncol_fun <- colorRamp2(c(-1, 0, 1), c(\\blue\\, \\grey\\, \\red\\))\nh1 <- Heatmap(data_matrix, name = \\loop score\\,\n        cluster_columns = FALSE,\n        cluster_rows = FALSE,\n        row_km = 9,\n        show_row_dend = FALSE,\n        border = FALSE,\n        show_row_names = FALSE,\n        col = col_fun)\n\nfileName <- here(figDir, paste0(\\heatmap_checkingDeltaAcrossCondition_OE\\))\npng(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 2, height = 10)\nprint(h1)\ndev.off()\n# \n# set.seed(123)\n# \n# hm_drawn <- draw(h1)\n# row_clusters <- row_order(hm_drawn)\n# \n# loop.cluster1 <- temp[row_clusters[[1]]]\n# loop.cluster2 <- temp[row_clusters[[2]]]\n# loop.cluster3 <- temp[row_clusters[[3]]]\n# loop.cluster4 <- temp[row_clusters[[4]]]\n# loop.cluster5 <- temp[row_clusters[[5]]]\n# loop.cluster6 <- temp[row_clusters[[6]]]\n# loop.cluster7 <- temp[row_clusters[[7]]]\n# loop.cluster8 <- temp[row_clusters[[8]]]\n# loop.cluster9 <- temp[row_clusters[[9]]]\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# Heatmap
library(circlize)
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Heatmap 2
set.seed(123)
# Import obs/exp scores and merge to the dataset
minValue <- -4
diffCutoff <- 0.5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
dataToPlot <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>%
dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO,
isdiff_dTAG_DMSO = case_when(log_obsexp_diff_dTAG_DMSO >= diffCutoff ~ 1,
abs(log_obsexp_diff_dTAG_DMSO) < diffCutoff ~ 0,
log_obsexp_diff_dTAG_DMSO <= diffCutoff ~ -1,
TRUE ~ NA ),
isdiff_A485_DMSO = case_when(log_obsexp_diff_A485_DMSO >= diffCutoff ~ 1,
abs(log_obsexp_diff_A485_DMSO) < diffCutoff ~ 0,
log_obsexp_diff_A485_DMSO <= diffCutoff ~ -1,
TRUE ~ NA))%>%
dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \grey\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 9,
show_row_dend = FALSE,
border = FALSE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_checkingDeltaAcrossCondition_OE\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 10)
print(h1)
dev.off()
#
# set.seed(123)
#
# hm_drawn <- draw(h1)
# row_clusters <- row_order(hm_drawn)
#
# loop.cluster1 <- temp[row_clusters[[1]]]
# loop.cluster2 <- temp[row_clusters[[2]]]
# loop.cluster3 <- temp[row_clusters[[3]]]
# loop.cluster4 <- temp[row_clusters[[4]]]
# loop.cluster5 <- temp[row_clusters[[5]]]
# loop.cluster6 <- temp[row_clusters[[6]]]
# loop.cluster7 <- temp[row_clusters[[7]]]
# loop.cluster8 <- temp[row_clusters[[8]]]
# loop.cluster9 <- temp[row_clusters[[9]]]
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n# Heatmap\nlibrary(circlize)\n\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n# Heatmap 2\nset.seed(123)\n\n# Import obs/exp scores and merge to the dataset\nminValue <- -4\ndiffCutoff <- 0.5\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n    dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                  log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                  log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))\n\n\n# Merge dataset\ndataToPlot <- data %>% dplyr::left_join(obsexp, by = c(\\id\\)) %>%\n  dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO,\n                isdiff_dTAG_DMSO = case_when(log_obsexp_diff_dTAG_DMSO >= diffCutoff ~ 1,\n                                             abs(log_obsexp_diff_dTAG_DMSO) < diffCutoff ~ 0,\n                                             log_obsexp_diff_dTAG_DMSO <= diffCutoff ~ -1,\n                                             TRUE ~ NA ),\n                isdiff_A485_DMSO = case_when(log_obsexp_diff_A485_DMSO >= diffCutoff ~ 1,\n                                             abs(log_obsexp_diff_A485_DMSO) < diffCutoff ~ 0,\n                                             log_obsexp_diff_A485_DMSO <= diffCutoff ~ -1,\n                                             TRUE ~ NA))%>%\n  dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)\n\n\n\ndata_matrix <- dataToPlot %>% column_to_rownames(var = \\id\\)%>% as.matrix()\ncol_fun <- colorRamp2(c(-1, 0, 1), c(\\blue\\, \\grey\\, \\red\\))\nh1 <- Heatmap(data_matrix, name = \\loop score\\,\n        cluster_columns = FALSE,\n        cluster_rows = FALSE,\n        row_km = 9,\n        show_row_dend = FALSE,\n        border = FALSE,\n        show_row_names = FALSE,\n        col = col_fun)\n\nfileName <- here(figDir, paste0(\\heatmap_checkingDeltaAcrossCondition_OE\\))\npng(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 2, height = 10)\nprint(h1)\ndev.off()\n# \n# set.seed(123)\n# \n# hm_drawn <- draw(h1)\n# row_clusters <- row_order(hm_drawn)\n# \n# loop.cluster1 <- temp[row_clusters[[1]]]\n# loop.cluster2 <- temp[row_clusters[[2]]]\n# loop.cluster3 <- temp[row_clusters[[3]]]\n# loop.cluster4 <- temp[row_clusters[[4]]]\n# loop.cluster5 <- temp[row_clusters[[5]]]\n# loop.cluster6 <- temp[row_clusters[[6]]]\n# loop.cluster7 <- temp[row_clusters[[7]]]\n# loop.cluster8 <- temp[row_clusters[[8]]]\n# loop.cluster9 <- temp[row_clusters[[9]]]\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
# Heatmap
library(circlize)
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Heatmap 2
set.seed(123)
# Import obs/exp scores and merge to the dataset
minValue <- -4
diffCutoff <- 0.5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
dataToPlot <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>%
dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO,
isdiff_dTAG_DMSO = case_when(log_obsexp_diff_dTAG_DMSO >= diffCutoff ~ 1,
abs(log_obsexp_diff_dTAG_DMSO) < diffCutoff ~ 0,
log_obsexp_diff_dTAG_DMSO <= diffCutoff ~ -1,
TRUE ~ NA ),
isdiff_A485_DMSO = case_when(log_obsexp_diff_A485_DMSO >= diffCutoff ~ 1,
abs(log_obsexp_diff_A485_DMSO) < diffCutoff ~ 0,
log_obsexp_diff_A485_DMSO <= diffCutoff ~ -1,
TRUE ~ NA))%>%
dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \grey\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 9,
show_row_dend = FALSE,
border = FALSE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_checkingDeltaAcrossCondition_OE\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 10)
print(h1)
dev.off()
#
# set.seed(123)
#
# hm_drawn <- draw(h1)
# row_clusters <- row_order(hm_drawn)
#
# loop.cluster1 <- temp[row_clusters[[1]]]
# loop.cluster2 <- temp[row_clusters[[2]]]
# loop.cluster3 <- temp[row_clusters[[3]]]
# loop.cluster4 <- temp[row_clusters[[4]]]
# loop.cluster5 <- temp[row_clusters[[5]]]
# loop.cluster6 <- temp[row_clusters[[6]]]
# loop.cluster7 <- temp[row_clusters[[7]]]
# loop.cluster8 <- temp[row_clusters[[8]]]
# loop.cluster9 <- temp[row_clusters[[9]]]
```
```
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n# Heatmap\nlibrary(circlize)\n\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n# Heatmap 2\nset.seed(123)\n\n# Import obs/exp scores and merge to the dataset\nminValue <- -4\ndiffCutoff <- 0.5\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n    dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                  log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                  log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))\n\n\n# Merge dataset\ndataToPlot <- data %>% dplyr::left_join(obsexp, by = c(\\id\\)) %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\)) %>%\n  dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO,\n                isdiff_dTAG_DMSO = case_when(log_obsexp_diff_dTAG_DMSO >= diffCutoff ~ 1,\n                                             abs(log_obsexp_diff_dTAG_DMSO) < diffCutoff ~ 0,\n                                             log_obsexp_diff_dTAG_DMSO <= diffCutoff ~ -1,\n                                             TRUE ~ NA ),\n                isdiff_A485_DMSO = case_when(log_obsexp_diff_A485_DMSO >= diffCutoff ~ 1,\n                                             abs(log_obsexp_diff_A485_DMSO) < diffCutoff ~ 0,\n                                             log_obsexp_diff_A485_DMSO <= diffCutoff ~ -1,\n                                             TRUE ~ NA))%>%\n  dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)\n\n\n\ndata_matrix <- dataToPlot %>% column_to_rownames(var = \\id\\)%>% as.matrix()\ncol_fun <- colorRamp2(c(-1, 0, 1), c(\\blue\\, \\grey\\, \\red\\))\nh1 <- Heatmap(data_matrix, name = \\loop score\\,\n        cluster_columns = FALSE,\n        cluster_rows = FALSE,\n        row_km = 9,\n        show_row_dend = FALSE,\n        border = FALSE,\n        show_row_names = FALSE,\n        col = col_fun)\n\nfileName <- here(figDir, paste0(\\heatmap_checkingDeltaAcrossCondition_reg_OE\\))\npng(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 2, height = 10)\nprint(h1)\ndev.off()\n# \n# set.seed(123)\n# \n# hm_drawn <- draw(h1)\n# row_clusters <- row_order(hm_drawn)\n# \n# loop.cluster1 <- temp[row_clusters[[1]]]\n# loop.cluster2 <- temp[row_clusters[[2]]]\n# loop.cluster3 <- temp[row_clusters[[3]]]\n# loop.cluster4 <- temp[row_clusters[[4]]]\n# loop.cluster5 <- temp[row_clusters[[5]]]\n# loop.cluster6 <- temp[row_clusters[[6]]]\n# loop.cluster7 <- temp[row_clusters[[7]]]\n# loop.cluster8 <- temp[row_clusters[[8]]]\n# loop.cluster9 <- temp[row_clusters[[9]]]\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# Heatmap
library(circlize)
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Heatmap 2
set.seed(123)
# Import obs/exp scores and merge to the dataset
minValue <- -4
diffCutoff <- 0.5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
dataToPlot <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>%
dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO,
isdiff_dTAG_DMSO = case_when(log_obsexp_diff_dTAG_DMSO >= diffCutoff ~ 1,
abs(log_obsexp_diff_dTAG_DMSO) < diffCutoff ~ 0,
log_obsexp_diff_dTAG_DMSO <= diffCutoff ~ -1,
TRUE ~ NA ),
isdiff_A485_DMSO = case_when(log_obsexp_diff_A485_DMSO >= diffCutoff ~ 1,
abs(log_obsexp_diff_A485_DMSO) < diffCutoff ~ 0,
log_obsexp_diff_A485_DMSO <= diffCutoff ~ -1,
TRUE ~ NA))%>%
dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \grey\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 9,
show_row_dend = FALSE,
border = FALSE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_checkingDeltaAcrossCondition_reg_OE\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 10)
print(h1)
dev.off()
#
# set.seed(123)
#
# hm_drawn <- draw(h1)
# row_clusters <- row_order(hm_drawn)
#
# loop.cluster1 <- temp[row_clusters[[1]]]
# loop.cluster2 <- temp[row_clusters[[2]]]
# loop.cluster3 <- temp[row_clusters[[3]]]
# loop.cluster4 <- temp[row_clusters[[4]]]
# loop.cluster5 <- temp[row_clusters[[5]]]
# loop.cluster6 <- temp[row_clusters[[6]]]
# loop.cluster7 <- temp[row_clusters[[7]]]
# loop.cluster8 <- temp[row_clusters[[8]]]
# loop.cluster9 <- temp[row_clusters[[9]]]
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n# Heatmap\nlibrary(circlize)\n\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n# Heatmap 2\nset.seed(123)\n\n# Import obs/exp scores and merge to the dataset\nminValue <- -4\ndiffCutoff <- 0.5\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n    dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                  log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                  log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))\n\n\n# Merge dataset\ndataToPlot <- data %>% dplyr::left_join(obsexp, by = c(\\id\\)) %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\)) %>%\n  dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO,\n                isdiff_dTAG_DMSO = case_when(log_obsexp_diff_dTAG_DMSO >= diffCutoff ~ 1,\n                                             abs(log_obsexp_diff_dTAG_DMSO) < diffCutoff ~ 0,\n                                             log_obsexp_diff_dTAG_DMSO <= diffCutoff ~ -1,\n                                             TRUE ~ NA ),\n                isdiff_A485_DMSO = case_when(log_obsexp_diff_A485_DMSO >= diffCutoff ~ 1,\n                                             abs(log_obsexp_diff_A485_DMSO) < diffCutoff ~ 0,\n                                             log_obsexp_diff_A485_DMSO <= diffCutoff ~ -1,\n                                             TRUE ~ NA))%>%\n  dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)\n\n\n\ndata_matrix <- dataToPlot %>% column_to_rownames(var = \\id\\)%>% as.matrix()\ncol_fun <- colorRamp2(c(-1, 0, 1), c(\\blue\\, \\grey\\, \\red\\))\nh1 <- Heatmap(data_matrix, name = \\loop score\\,\n        cluster_columns = FALSE,\n        cluster_rows = FALSE,\n        row_km = 9,\n        show_row_dend = FALSE,\n        border = FALSE,\n        show_row_names = FALSE,\n        col = col_fun)\n\nfileName <- here(figDir, paste0(\\heatmap_checkingDeltaAcrossCondition_reg_OE\\))\npng(paste0(fileName, \\.png\\), res = 600, units = \\in\\, width = 2, height = 10)\nprint(h1)\ndev.off()\n# \n# set.seed(123)\n# \n# hm_drawn <- draw(h1)\n# row_clusters <- row_order(hm_drawn)\n# \n# loop.cluster1 <- temp[row_clusters[[1]]]\n# loop.cluster2 <- temp[row_clusters[[2]]]\n# loop.cluster3 <- temp[row_clusters[[3]]]\n# loop.cluster4 <- temp[row_clusters[[4]]]\n# loop.cluster5 <- temp[row_clusters[[5]]]\n# loop.cluster6 <- temp[row_clusters[[6]]]\n# loop.cluster7 <- temp[row_clusters[[7]]]\n# loop.cluster8 <- temp[row_clusters[[8]]]\n# loop.cluster9 <- temp[row_clusters[[9]]]\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
# Heatmap
library(circlize)
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Heatmap 2
set.seed(123)
# Import obs/exp scores and merge to the dataset
minValue <- -4
diffCutoff <- 0.5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
dataToPlot <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\)) %>%
dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO,
isdiff_dTAG_DMSO = case_when(log_obsexp_diff_dTAG_DMSO >= diffCutoff ~ 1,
abs(log_obsexp_diff_dTAG_DMSO) < diffCutoff ~ 0,
log_obsexp_diff_dTAG_DMSO <= diffCutoff ~ -1,
TRUE ~ NA ),
isdiff_A485_DMSO = case_when(log_obsexp_diff_A485_DMSO >= diffCutoff ~ 1,
abs(log_obsexp_diff_A485_DMSO) < diffCutoff ~ 0,
log_obsexp_diff_A485_DMSO <= diffCutoff ~ -1,
TRUE ~ NA))%>%
dplyr::select(id, isdiff_dTAG_DMSO, isdiff_A485_DMSO)
data_matrix <- dataToPlot %>% column_to_rownames(var = \id\)%>% as.matrix()
col_fun <- colorRamp2(c(-1, 0, 1), c(\blue\, \grey\, \red\))
h1 <- Heatmap(data_matrix, name = \loop score\,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_km = 9,
show_row_dend = FALSE,
border = FALSE,
show_row_names = FALSE,
col = col_fun)
fileName <- here(figDir, paste0(\heatmap_checkingDeltaAcrossCondition_reg_OE\))
png(paste0(fileName, \.png\), res = 600, units = \in\, width = 2, height = 10)
print(h1)
dev.off()
#
# set.seed(123)
#
# hm_drawn <- draw(h1)
# row_clusters <- row_order(hm_drawn)
#
# loop.cluster1 <- temp[row_clusters[[1]]]
# loop.cluster2 <- temp[row_clusters[[2]]]
# loop.cluster3 <- temp[row_clusters[[3]]]
# loop.cluster4 <- temp[row_clusters[[4]]]
# loop.cluster5 <- temp[row_clusters[[5]]]
# loop.cluster6 <- temp[row_clusters[[6]]]
# loop.cluster7 <- temp[row_clusters[[7]]]
# loop.cluster8 <- temp[row_clusters[[8]]]
# loop.cluster9 <- temp[row_clusters[[9]]]
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### All loops _OE
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nmarginError <- function(myList) {\n  sample.n = length(myList)\n  sample.sd = sd(myList)\n  sample.se = sample.sd/sqrt(sample.n)\n  alpha = 0.05\n  degrees.freedom = sample.n - 1\n  t.score = qt(p = alpha/2, df = degrees.freedom, lower.tail = F)\n  margin.error = t.score*sample.se\n  return(margin.error)\n}\n\nPROseq.bobbie <- as_tibble(fread(here(refDir, \\bobbie_gene_classification.csv\\)))\n\ngeneList.Epi.dTAG.up <- (fread(here(refDir, \\diff_G1.dTAG_G1.Epi.dTAG_vs_G1.Epi.DMSO.tsv\\)) %>% dplyr::filter(padj < 0.05, log2FoldChange > 0))$ensembl_gene_id\n\ngeneList.Epi.dTAG.down <- (fread(here(refDir, \\diff_G1.dTAG_G1.Epi.dTAG_vs_G1.Epi.DMSO.tsv\\)) %>% dplyr::filter(padj < 0.05, log2FoldChange < 0))$ensembl_gene_id\n\n## Converting transcript ID to gene ID\nidPair_tg <- getBM(attributes = c(\\ensembl_transcript_id\\, \\ensembl_gene_id\\),\n                 filters = \\ensembl_transcript_id\\,\n                 values = PROseq.bobbie$enst,\n                 mart = ensembl.v102)\nPROseq.bobbie <- PROseq.bobbie %>% dplyr::left_join(idPair_tg, by = c(\\enst\\ = \\ensembl_transcript_id\\))\n\n## Bar Plot\ntemp <- PROseq.bobbie %>% dplyr::filter(ensembl_gene_id %in% geneList.Epi.dTAG.down)\n\ngroup <- c(rep(\\EpiLC_dTAGvsDMSO_DOWN\\, 4))\ncluster <- rep(c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\), 1)\ncluster <- factor(cluster, levels = c(\\Early\\, \\Middle\\, \\Late\\, \\Transient\\))\nvalue <- c(sum(temp$Cluster == \\Early\\),\n           sum(temp$Cluster == \\Middle\\),\n           sum(temp$Cluster == \\Late\\),\n           sum(temp$Cluster == \\Transient\\))\ndata <- data.frame(group, cluster, value)\nggplot(data, aes(fill=cluster, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n## Line plot\ntemp <- temp %>% dplyr::select(c(4, 5, 6, 7, 8, 9))\ntemp.tall = temp %>% pivot_longer(-c(1, 2), names_to = \\timepoints\\, values_to = \\value\\)\n\ngg1 = ggplot(temp.tall,\n       aes( x = factor(timepoints, level = c(\\MIT\\, \\EG1\\, \\LG1\\, \\ASYN\\)),\n            y = value,\n            group = enst, col = enst)) +\n  geom_line() +\n  theme_classic() +\n  ggtitle(\\Bobbie\n"} -->
marginError <- function(myList) {
sample.n = length(myList)
sample.sd = sd(myList)
sample.se = sample.sd/sqrt(sample.n)
alpha = 0.05
degrees.freedom = sample.n - 1
t.score = qt(p = alpha/2, df = degrees.freedom, lower.tail = F)
margin.error = t.score*sample.se
return(margin.error)
}
PROseq.bobbie <- as_tibble(fread(here(refDir, \bobbie_gene_classification.csv\)))
geneList.Epi.dTAG.up <- (fread(here(refDir, \diff_G1.dTAG_G1.Epi.dTAG_vs_G1.Epi.DMSO.tsv\)) %>% dplyr::filter(padj < 0.05, log2FoldChange > 0))$ensembl_gene_id
geneList.Epi.dTAG.down <- (fread(here(refDir, \diff_G1.dTAG_G1.Epi.dTAG_vs_G1.Epi.DMSO.tsv\)) %>% dplyr::filter(padj < 0.05, log2FoldChange < 0))$ensembl_gene_id
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = PROseq.bobbie$enst,
mart = ensembl.v102)
PROseq.bobbie <- PROseq.bobbie %>% dplyr::left_join(idPair_tg, by = c(\enst\ = \ensembl_transcript_id\))
## Bar Plot
temp <- PROseq.bobbie %>% dplyr::filter(ensembl_gene_id %in% geneList.Epi.dTAG.down)
group <- c(rep(\EpiLC_dTAGvsDMSO_DOWN\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 1)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(sum(temp$Cluster == \Early\),
sum(temp$Cluster == \Middle\),
sum(temp$Cluster == \Late\),
sum(temp$Cluster == \Transient\))
data <- data.frame(group, cluster, value)
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
## Line plot
temp <- temp %>% dplyr::select(c(4, 5, 6, 7, 8, 9))
temp.tall = temp %>% pivot_longer(-c(1, 2), names_to = \timepoints\, values_to = \value\)
gg1 = ggplot(temp.tall,
aes( x = factor(timepoints, level = c(\MIT\, \EG1\, \LG1\, \ASYN\)),
y = value,
group = enst, col = enst)) +
geom_line() +
theme_classic() +
ggtitle(\Bobbie```r
marginError <- function(myList) {
sample.n = length(myList)
sample.sd = sd(myList)
sample.se = sample.sd/sqrt(sample.n)
alpha = 0.05
degrees.freedom = sample.n - 1
t.score = qt(p = alpha/2, df = degrees.freedom, lower.tail = F)
margin.error = t.score*sample.se
return(margin.error)
}
PROseq.bobbie <- as_tibble(fread(here(refDir, \bobbie_gene_classification.csv\)))
geneList.Epi.dTAG.up <- (fread(here(refDir, \diff_G1.dTAG_G1.Epi.dTAG_vs_G1.Epi.DMSO.tsv\)) %>% dplyr::filter(padj < 0.05, log2FoldChange > 0))$ensembl_gene_id
geneList.Epi.dTAG.down <- (fread(here(refDir, \diff_G1.dTAG_G1.Epi.dTAG_vs_G1.Epi.DMSO.tsv\)) %>% dplyr::filter(padj < 0.05, log2FoldChange < 0))$ensembl_gene_id
## Converting transcript ID to gene ID
idPair_tg <- getBM(attributes = c(\ensembl_transcript_id\, \ensembl_gene_id\),
filters = \ensembl_transcript_id\,
values = PROseq.bobbie$enst,
mart = ensembl.v102)
PROseq.bobbie <- PROseq.bobbie %>% dplyr::left_join(idPair_tg, by = c(\enst\ = \ensembl_transcript_id\))
## Bar Plot
temp <- PROseq.bobbie %>% dplyr::filter(ensembl_gene_id %in% geneList.Epi.dTAG.down)
group <- c(rep(\EpiLC_dTAGvsDMSO_DOWN\, 4))
cluster <- rep(c(\Early\, \Middle\, \Late\, \Transient\), 1)
cluster <- factor(cluster, levels = c(\Early\, \Middle\, \Late\, \Transient\))
value <- c(sum(temp$Cluster == \Early\),
sum(temp$Cluster == \Middle\),
sum(temp$Cluster == \Late\),
sum(temp$Cluster == \Transient\))
data <- data.frame(group, cluster, value)
ggplot(data, aes(fill=cluster, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
## Line plot
temp <- temp %>% dplyr::select(c(4, 5, 6, 7, 8, 9))
temp.tall = temp %>% pivot_longer(-c(1, 2), names_to = \timepoints\, values_to = \value\)
gg1 = ggplot(temp.tall,
aes( x = factor(timepoints, level = c(\MIT\, \EG1\, \LG1\, \ASYN\)),
y = value,
group = enst, col = enst)) +
geom_line() +
theme_classic() +
ggtitle(\Bobbie
<!-- rnb-source-begin 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 -->
```r
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS)
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.tb <- gene.tb %>% dplyr::filter(ensembl %in% c(group1, group2))
genes.gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),
ensembl = gene.tb$ensembl
)
genebody.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(genebody.tb) <- c(\chr\, \start\, \end\)
genebody.gr <- makeGRangesFromDataFrame(genebody.tb)
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
peak.H3K27ac<- makeGRangesFromDataFrame(fread(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\)) %>%
dplyr::mutate(chr = V1, start = (V2 + V3)/2, end = (V2 + V3)/2) %>%
dplyr::select(chr, start, end))
####### Filtering out peaks overlapping with the gene body
overlaps <- findOverlaps(genebody.gr, peak.H3K27ac)
# Indices of peaks that overlap the TSS
overlapping_peak_indices <- unique(subjectHits(overlaps))
# Exclude overlapping peaks
non_overlapping_peaks <- peak.H3K27ac[-overlapping_peak_indices]
####### Calculating distance to nearest peak
nearest_peak_indices <- nearest(genes.gr, non_overlapping_peaks)
nearest_peaks <- non_overlapping_peaks[nearest_peak_indices]
distances <- distance(genes.gr, nearest_peaks)
results <- data.frame(
ensembl = mcols(genes.gr)$ensembl,
gene_chr = as.character(seqnames(genes.gr)),
gene_TSS = start(genes.gr),
peak_chr = as.character(seqnames(nearest_peaks)),
peak_start = start(nearest_peaks),
peak_end = end(nearest_peaks),
distance = distances
)
results <- results %>% dplyr::mutate(group = case_when(ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA))
ggplot(results, aes(x = group, y = distance, fill = group)) + geom_violin() + geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) + theme_bw() + ggtitle(\distance to nearest H3K27ac peak excluding gene body\) + scale_y_continuous(labels = label_kb_mb) + coord_cartesian(ylim = c(0, 250*1000))
```
<!-- rnb-source-end -->```r
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS)
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.tb <- gene.tb %>% dplyr::filter(ensembl %in% c(group1, group2))
genes.gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),
ensembl = gene.tb$ensembl
)
genebody.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(genebody.tb) <- c(\chr\, \start\, \end\)
genebody.gr <- makeGRangesFromDataFrame(genebody.tb)
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
peak.H3K27ac<- makeGRangesFromDataFrame(fread(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\)) %>%
dplyr::mutate(chr = V1, start = (V2 + V3)/2, end = (V2 + V3)/2) %>%
dplyr::select(chr, start, end))
####### Filtering out peaks overlapping with the gene body
overlaps <- findOverlaps(genebody.gr, peak.H3K27ac)
# Indices of peaks that overlap the TSS
overlapping_peak_indices <- unique(subjectHits(overlaps))
# Exclude overlapping peaks
non_overlapping_peaks <- peak.H3K27ac[-overlapping_peak_indices]
####### Calculating distance to nearest peak
nearest_peak_indices <- nearest(genes.gr, non_overlapping_peaks)
nearest_peaks <- non_overlapping_peaks[nearest_peak_indices]
distances <- distance(genes.gr, nearest_peaks)
results <- data.frame(
ensembl = mcols(genes.gr)$ensembl,
gene_chr = as.character(seqnames(genes.gr)),
gene_TSS = start(genes.gr),
peak_chr = as.character(seqnames(nearest_peaks)),
peak_start = start(nearest_peaks),
peak_end = end(nearest_peaks),
distance = distances
)
results <- results %>% dplyr::mutate(group = case_when(ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA))
ggplot(results, aes(x = group, y = distance, fill = group)) + geom_violin() + geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) + theme_bw() + ggtitle(\distance to nearest H3K27ac peak excluding gene body\) + scale_y_continuous(labels = label_kb_mb) + coord_cartesian(ylim = c(0, 250*1000))
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.32] Comparing with Bobbie's data
Where does differentiated related genes fall into?
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3), ensembl = V6, chr = V1) %>%\n  dplyr::select(ensembl, chr, TSS) %>% dplyr::filter(ensembl %in% c(group1, group2))\ngenes.gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),\n  ensembl = gene.tb$ensembl\n)\n\ngenebody.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>% dplyr::filter(V6 %in% c(group1, group2))%>% dplyr::select(V1, V2, V3)\ncolnames(genebody.tb) <- c(\\chr\\, \\start\\, \\end\\)\ngenebody.gr <- makeGRangesFromDataFrame(genebody.tb)\n\n\n# Step 1: Find overlaps between gene bodies and peaks\noverlaps <- findOverlaps(genebody.gr, peak.H3K27ac)\n\n# Create a list mapping each gene to the indices of peaks overlapping with its gene body\noverlapping_peaks_per_gene <- split(subjectHits(overlaps), queryHits(overlaps))\n\n# Initialize an empty list to store results\nresults_list <- vector(\\list\\, length(genes.gr))\n\n# Step 2: For each gene, exclude overlapping peaks and find the nearest peak to its TSS\nfor (i in seq_along(genes.gr)) {\n  gene <- genes.gr[i]\n  \n  # Get indices of peaks overlapping with this gene's body\n  overlapping_peak_indices <- overlapping_peaks_per_gene[[as.character(i)]]\n  \n  # Exclude overlapping peaks for this gene\n  if (!is.null(overlapping_peak_indices)) {\n    peaks_to_consider <- peak.H3K27ac[-overlapping_peak_indices]\n  } else {\n    peaks_to_consider <- peak.H3K27ac\n  }\n  \n  # Find the nearest peak to the TSS of this gene\n  nearest_peak_index <- nearest(gene, peaks_to_consider)\n  \n  if (is.na(nearest_peak_index)) {\n    # No peaks found; set NA values\n    results_list[[i]] <- data.frame(\n      ensembl = mcols(gene)$ensembl,\n      gene_chr = as.character(seqnames(gene)),\n      gene_TSS = start(gene),\n      peak_chr = NA,\n      peak_start = NA,\n      peak_end = NA,\n      distance = NA\n    )\n  } else {\n    nearest_peak <- peaks_to_consider[nearest_peak_index]\n    dist <- distance(gene, nearest_peak)\n    \n    results_list[[i]] <- data.frame(\n      ensembl = mcols(gene)$ensembl,\n      gene_chr = as.character(seqnames(gene)),\n      gene_TSS = start(gene),\n      peak_chr = as.character(seqnames(nearest_peak)),\n      peak_start = start(nearest_peak),\n      peak_end = end(nearest_peak),\n      distance = dist\n    )\n  }\n}\n\n# Combine results into a single data frame\nresults <- do.call(rbind, results_list)\n\n# Add group information\nresults <- results %>% dplyr::mutate(group = case_when(\n  ensembl %in% group1 ~ \\group1\\,\n  ensembl %in% group2 ~ \\group2\\,\n  TRUE ~ NA_character_\n))\n\n# Plotting\nggplot(results, aes(x = group, y = distance, fill = group)) +\n  geom_violin() +\n  geom_boxplot(outlier.shape = NA, width = 0.1, fill = \\white\\) +\n  theme_bw() +\n  ggtitle(\\Distance to Nearest H3K27ac Peak Excluding Gene Body Overlaps\\) +\n  scale_y_continuous(labels = label_kb_mb) +\n  coord_cartesian(ylim = c(0, 50 * 1000))\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS) %>% dplyr::filter(ensembl %in% c(group1, group2))
genes.gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),
ensembl = gene.tb$ensembl
)
genebody.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>% dplyr::filter(V6 %in% c(group1, group2))%>% dplyr::select(V1, V2, V3)
colnames(genebody.tb) <- c(\chr\, \start\, \end\)
genebody.gr <- makeGRangesFromDataFrame(genebody.tb)
# Step 1: Find overlaps between gene bodies and peaks
overlaps <- findOverlaps(genebody.gr, peak.H3K27ac)
# Create a list mapping each gene to the indices of peaks overlapping with its gene body
overlapping_peaks_per_gene <- split(subjectHits(overlaps), queryHits(overlaps))
# Initialize an empty list to store results
results_list <- vector(\list\, length(genes.gr))
# Step 2: For each gene, exclude overlapping peaks and find the nearest peak to its TSS
for (i in seq_along(genes.gr)) {
gene <- genes.gr[i]
# Get indices of peaks overlapping with this gene's body
overlapping_peak_indices <- overlapping_peaks_per_gene[[as.character(i)]]
# Exclude overlapping peaks for this gene
if (!is.null(overlapping_peak_indices)) {
peaks_to_consider <- peak.H3K27ac[-overlapping_peak_indices]
} else {
peaks_to_consider <- peak.H3K27ac
}
# Find the nearest peak to the TSS of this gene
nearest_peak_index <- nearest(gene, peaks_to_consider)
if (is.na(nearest_peak_index)) {
# No peaks found; set NA values
results_list[[i]] <- data.frame(
ensembl = mcols(gene)$ensembl,
gene_chr = as.character(seqnames(gene)),
gene_TSS = start(gene),
peak_chr = NA,
peak_start = NA,
peak_end = NA,
distance = NA
)
} else {
nearest_peak <- peaks_to_consider[nearest_peak_index]
dist <- distance(gene, nearest_peak)
results_list[[i]] <- data.frame(
ensembl = mcols(gene)$ensembl,
gene_chr = as.character(seqnames(gene)),
gene_TSS = start(gene),
peak_chr = as.character(seqnames(nearest_peak)),
peak_start = start(nearest_peak),
peak_end = end(nearest_peak),
distance = dist
)
}
}
# Combine results into a single data frame
results <- do.call(rbind, results_list)
# Add group information
results <- results %>% dplyr::mutate(group = case_when(
ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA_character_
))
# Plotting
ggplot(results, aes(x = group, y = distance, fill = group)) +
geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) +
theme_bw() +
ggtitle(\Distance to Nearest H3K27ac Peak Excluding Gene Body Overlaps\) +
scale_y_continuous(labels = label_kb_mb) +
coord_cartesian(ylim = c(0, 50 * 1000))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3), ensembl = V6, chr = V1) %>%\n  dplyr::select(ensembl, chr, TSS) %>% dplyr::filter(ensembl %in% c(group1, group2))\ngenes.gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),\n  ensembl = gene.tb$ensembl\n)\n\ngenebody.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>% dplyr::filter(V6 %in% c(group1, group2))%>% dplyr::select(V1, V2, V3)\ncolnames(genebody.tb) <- c(\\chr\\, \\start\\, \\end\\)\ngenebody.gr <- makeGRangesFromDataFrame(genebody.tb)\n\n\n# Step 1: Find overlaps between gene bodies and peaks\noverlaps <- findOverlaps(genebody.gr, peak.H3K27ac)\n\n# Create a list mapping each gene to the indices of peaks overlapping with its gene body\noverlapping_peaks_per_gene <- split(subjectHits(overlaps), queryHits(overlaps))\n\n# Initialize an empty list to store results\nresults_list <- vector(\\list\\, length(genes.gr))\n\n# Step 2: For each gene, exclude overlapping peaks and find the nearest peak to its TSS\nfor (i in seq_along(genes.gr)) {\n  gene <- genes.gr[i]\n  \n  # Get indices of peaks overlapping with this gene's body\n  overlapping_peak_indices <- overlapping_peaks_per_gene[[as.character(i)]]\n  \n  # Exclude overlapping peaks for this gene\n  if (!is.null(overlapping_peak_indices)) {\n    peaks_to_consider <- peak.H3K27ac[-overlapping_peak_indices]\n  } else {\n    peaks_to_consider <- peak.H3K27ac\n  }\n  \n  # Find the nearest peak to the TSS of this gene\n  nearest_peak_index <- nearest(gene, peaks_to_consider)\n  \n  if (is.na(nearest_peak_index)) {\n    # No peaks found; set NA values\n    results_list[[i]] <- data.frame(\n      ensembl = mcols(gene)$ensembl,\n      gene_chr = as.character(seqnames(gene)),\n      gene_TSS = start(gene),\n      peak_chr = NA,\n      peak_start = NA,\n      peak_end = NA,\n      distance = NA\n    )\n  } else {\n    nearest_peak <- peaks_to_consider[nearest_peak_index]\n    dist <- distance(gene, nearest_peak)\n    \n    results_list[[i]] <- data.frame(\n      ensembl = mcols(gene)$ensembl,\n      gene_chr = as.character(seqnames(gene)),\n      gene_TSS = start(gene),\n      peak_chr = as.character(seqnames(nearest_peak)),\n      peak_start = start(nearest_peak),\n      peak_end = end(nearest_peak),\n      distance = dist\n    )\n  }\n}\n\n# Combine results into a single data frame\nresults <- do.call(rbind, results_list)\n\n# Add group information\nresults <- results %>% dplyr::mutate(group = case_when(\n  ensembl %in% group1 ~ \\group1\\,\n  ensembl %in% group2 ~ \\group2\\,\n  TRUE ~ NA_character_\n))\n\n# Plotting\nggplot(results, aes(x = group, y = distance, fill = group)) +\n  geom_violin() +\n  geom_boxplot(outlier.shape = NA, width = 0.1, fill = \\white\\) +\n  theme_bw() +\n  ggtitle(\\Distance to Nearest H3K27ac Peak Excluding Gene Body Overlaps\\) +\n  scale_y_continuous(labels = label_kb_mb) +\n  coord_cartesian(ylim = c(0, 50 * 1000))\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS) %>% dplyr::filter(ensembl %in% c(group1, group2))
genes.gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),
ensembl = gene.tb$ensembl
)
genebody.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>% dplyr::filter(V6 %in% c(group1, group2))%>% dplyr::select(V1, V2, V3)
colnames(genebody.tb) <- c(\chr\, \start\, \end\)
genebody.gr <- makeGRangesFromDataFrame(genebody.tb)
# Step 1: Find overlaps between gene bodies and peaks
overlaps <- findOverlaps(genebody.gr, peak.H3K27ac)
# Create a list mapping each gene to the indices of peaks overlapping with its gene body
overlapping_peaks_per_gene <- split(subjectHits(overlaps), queryHits(overlaps))
# Initialize an empty list to store results
results_list <- vector(\list\, length(genes.gr))
# Step 2: For each gene, exclude overlapping peaks and find the nearest peak to its TSS
for (i in seq_along(genes.gr)) {
gene <- genes.gr[i]
# Get indices of peaks overlapping with this gene's body
overlapping_peak_indices <- overlapping_peaks_per_gene[[as.character(i)]]
# Exclude overlapping peaks for this gene
if (!is.null(overlapping_peak_indices)) {
peaks_to_consider <- peak.H3K27ac[-overlapping_peak_indices]
} else {
peaks_to_consider <- peak.H3K27ac
}
# Find the nearest peak to the TSS of this gene
nearest_peak_index <- nearest(gene, peaks_to_consider)
if (is.na(nearest_peak_index)) {
# No peaks found; set NA values
results_list[[i]] <- data.frame(
ensembl = mcols(gene)$ensembl,
gene_chr = as.character(seqnames(gene)),
gene_TSS = start(gene),
peak_chr = NA,
peak_start = NA,
peak_end = NA,
distance = NA
)
} else {
nearest_peak <- peaks_to_consider[nearest_peak_index]
dist <- distance(gene, nearest_peak)
results_list[[i]] <- data.frame(
ensembl = mcols(gene)$ensembl,
gene_chr = as.character(seqnames(gene)),
gene_TSS = start(gene),
peak_chr = as.character(seqnames(nearest_peak)),
peak_start = start(nearest_peak),
peak_end = end(nearest_peak),
distance = dist
)
}
}
# Combine results into a single data frame
results <- do.call(rbind, results_list)
# Add group information
results <- results %>% dplyr::mutate(group = case_when(
ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA_character_
))
# Plotting
ggplot(results, aes(x = group, y = distance, fill = group)) +
geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) +
theme_bw() +
ggtitle(\Distance to Nearest H3K27ac Peak Excluding Gene Body Overlaps\) +
scale_y_continuous(labels = label_kb_mb) +
coord_cartesian(ylim = c(0, 50 * 1000))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###[2.33] Finding closest enhancer
Here, enhancer will be defined by H3K27ac peak
It makes more sence to include the enhancer peak on gene body
(strategy 1) exclude +-10kb region from TSS only
(strategy 2) only check the E-P loops called in Micro-C
##### binary group - excluding all gene body
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3), ensembl = V6, chr = V1) %>%\n  dplyr::select(ensembl, chr, TSS)\n\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.tb <- gene.tb %>% dplyr::filter(ensembl %in% c(group1, group2))\ngenes.gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),\n  ensembl = gene.tb$ensembl\n)\n\ngenebody.tb <- gene.tb %>% dplyr::mutate(start = TSS-10*1000, end = TSS + 10*1000) %>%\n  dplyr::select(chr, start, end)\n\ngenebody.gr <- makeGRangesFromDataFrame(genebody.tb)\n\n\npeak.H3K27ac <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n# peak.H3K27ac<- makeGRangesFromDataFrame(fread(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\)) %>% \n#                                           dplyr::mutate(chr = V1, start = (V2 + V3)/2, end = (V2 + V3)/2) %>%\n#                                           dplyr::select(chr, start, end))\n\n####### Filtering out peaks overlapping with the gene body\noverlaps <- findOverlaps(genebody.gr, peak.H3K27ac)\n\n# Indices of peaks that overlap the TSS\noverlapping_peak_indices <- unique(subjectHits(overlaps))\n\n# Exclude overlapping peaks\nnon_overlapping_peaks <- peak.H3K27ac[-overlapping_peak_indices]\n\n\n####### Calculating distance to nearest peak\nnearest_peak_indices <- nearest(genes.gr, non_overlapping_peaks)\nnearest_peaks <- non_overlapping_peaks[nearest_peak_indices]\ndistances <- distance(genes.gr, nearest_peaks)\n\n\nresults <- data.frame(\n  ensembl = mcols(genes.gr)$ensembl,\n  gene_chr = as.character(seqnames(genes.gr)),\n  gene_TSS = start(genes.gr),\n  peak_chr = as.character(seqnames(nearest_peaks)),\n  peak_start = start(nearest_peaks),\n  peak_end = end(nearest_peaks),\n  distance = distances\n)\n\nresults <- results %>% dplyr::mutate(group = case_when(ensembl %in% group1 ~ \\group1\\,\n                                            ensembl %in% group2 ~ \\group2\\,\n                                            TRUE ~ NA))\n\nggplot(results, aes(x = group, y = distance, fill = group)) + geom_violin() + geom_boxplot(outlier.shape = NA, width = 0.1, fill = \\white\\) + theme_bw() + ggtitle(\\distance to nearest H3K27ac peak excluding gene body\\) + scale_y_continuous(labels = label_kb_mb) + coord_cartesian(ylim = c(0, 250*1000))\n\n  \n```\n\n<!-- rnb-source-end -->\n"} -->
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS)
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.tb <- gene.tb %>% dplyr::filter(ensembl %in% c(group1, group2))
genes.gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),
ensembl = gene.tb$ensembl
)
genebody.tb <- gene.tb %>% dplyr::mutate(start = TSS-10*1000, end = TSS + 10*1000) %>%
dplyr::select(chr, start, end)
genebody.gr <- makeGRangesFromDataFrame(genebody.tb)
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
# peak.H3K27ac<- makeGRangesFromDataFrame(fread(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\)) %>%
# dplyr::mutate(chr = V1, start = (V2 + V3)/2, end = (V2 + V3)/2) %>%
# dplyr::select(chr, start, end))
####### Filtering out peaks overlapping with the gene body
overlaps <- findOverlaps(genebody.gr, peak.H3K27ac)
# Indices of peaks that overlap the TSS
overlapping_peak_indices <- unique(subjectHits(overlaps))
# Exclude overlapping peaks
non_overlapping_peaks <- peak.H3K27ac[-overlapping_peak_indices]
####### Calculating distance to nearest peak
nearest_peak_indices <- nearest(genes.gr, non_overlapping_peaks)
nearest_peaks <- non_overlapping_peaks[nearest_peak_indices]
distances <- distance(genes.gr, nearest_peaks)
results <- data.frame(
ensembl = mcols(genes.gr)$ensembl,
gene_chr = as.character(seqnames(genes.gr)),
gene_TSS = start(genes.gr),
peak_chr = as.character(seqnames(nearest_peaks)),
peak_start = start(nearest_peaks),
peak_end = end(nearest_peaks),
distance = distances
)
results <- results %>% dplyr::mutate(group = case_when(ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA))
ggplot(results, aes(x = group, y = distance, fill = group)) + geom_violin() + geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) + theme_bw() + ggtitle(\distance to nearest H3K27ac peak excluding gene body\) + scale_y_continuous(labels = label_kb_mb) + coord_cartesian(ylim = c(0, 250*1000))
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3), ensembl = V6, chr = V1) %>%\n  dplyr::select(ensembl, chr, TSS)\n\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.tb <- gene.tb %>% dplyr::filter(ensembl %in% c(group1, group2))\ngenes.gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),\n  ensembl = gene.tb$ensembl\n)\n\ngenebody.tb <- gene.tb %>% dplyr::mutate(start = TSS-10*1000, end = TSS + 10*1000) %>%\n  dplyr::select(chr, start, end)\n\ngenebody.gr <- makeGRangesFromDataFrame(genebody.tb)\n\n\npeak.H3K27ac <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n# peak.H3K27ac<- makeGRangesFromDataFrame(fread(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\)) %>% \n#                                           dplyr::mutate(chr = V1, start = (V2 + V3)/2, end = (V2 + V3)/2) %>%\n#                                           dplyr::select(chr, start, end))\n\n####### Filtering out peaks overlapping with the gene body\noverlaps <- findOverlaps(genebody.gr, peak.H3K27ac)\n\n# Indices of peaks that overlap the TSS\noverlapping_peak_indices <- unique(subjectHits(overlaps))\n\n# Exclude overlapping peaks\nnon_overlapping_peaks <- peak.H3K27ac[-overlapping_peak_indices]\n\n\n####### Calculating distance to nearest peak\nnearest_peak_indices <- nearest(genes.gr, non_overlapping_peaks)\nnearest_peaks <- non_overlapping_peaks[nearest_peak_indices]\ndistances <- distance(genes.gr, nearest_peaks)\n\n\nresults <- data.frame(\n  ensembl = mcols(genes.gr)$ensembl,\n  gene_chr = as.character(seqnames(genes.gr)),\n  gene_TSS = start(genes.gr),\n  peak_chr = as.character(seqnames(nearest_peaks)),\n  peak_start = start(nearest_peaks),\n  peak_end = end(nearest_peaks),\n  distance = distances\n)\n\nresults <- results %>% dplyr::mutate(group = case_when(ensembl %in% group1 ~ \\group1\\,\n                                            ensembl %in% group2 ~ \\group2\\,\n                                            TRUE ~ NA))\n\nggplot(results, aes(x = group, y = distance, fill = group)) + geom_violin() + geom_boxplot(outlier.shape = NA, width = 0.1, fill = \\white\\) + theme_bw() + ggtitle(\\distance to nearest H3K27ac peak excluding gene body\\) + scale_y_continuous(labels = label_kb_mb) + coord_cartesian(ylim = c(0, 250*1000))\n\n  \n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS)
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.tb <- gene.tb %>% dplyr::filter(ensembl %in% c(group1, group2))
genes.gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),
ensembl = gene.tb$ensembl
)
genebody.tb <- gene.tb %>% dplyr::mutate(start = TSS-10*1000, end = TSS + 10*1000) %>%
dplyr::select(chr, start, end)
genebody.gr <- makeGRangesFromDataFrame(genebody.tb)
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
# peak.H3K27ac<- makeGRangesFromDataFrame(fread(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\)) %>%
# dplyr::mutate(chr = V1, start = (V2 + V3)/2, end = (V2 + V3)/2) %>%
# dplyr::select(chr, start, end))
####### Filtering out peaks overlapping with the gene body
overlaps <- findOverlaps(genebody.gr, peak.H3K27ac)
# Indices of peaks that overlap the TSS
overlapping_peak_indices <- unique(subjectHits(overlaps))
# Exclude overlapping peaks
non_overlapping_peaks <- peak.H3K27ac[-overlapping_peak_indices]
####### Calculating distance to nearest peak
nearest_peak_indices <- nearest(genes.gr, non_overlapping_peaks)
nearest_peaks <- non_overlapping_peaks[nearest_peak_indices]
distances <- distance(genes.gr, nearest_peaks)
results <- data.frame(
ensembl = mcols(genes.gr)$ensembl,
gene_chr = as.character(seqnames(genes.gr)),
gene_TSS = start(genes.gr),
peak_chr = as.character(seqnames(nearest_peaks)),
peak_start = start(nearest_peaks),
peak_end = end(nearest_peaks),
distance = distances
)
results <- results %>% dplyr::mutate(group = case_when(ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA))
ggplot(results, aes(x = group, y = distance, fill = group)) + geom_violin() + geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) + theme_bw() + ggtitle(\distance to nearest H3K27ac peak excluding gene body\) + scale_y_continuous(labels = label_kb_mb) + coord_cartesian(ylim = c(0, 250*1000))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### binary group - excluding specific gene body
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3), ensembl = V6, chr = V1) %>%\n  dplyr::select(ensembl, chr, TSS) %>% dplyr::filter(ensembl %in% c(group1, group2))\ngenes.gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),\n  ensembl = gene.tb$ensembl\n)\n\ngenebody.tb <- gene.tb %>% dplyr::mutate(start = TSS-10*1000, end = TSS + 10*1000) %>%\n  dplyr::select(chr, start, end)\ngenebody.gr <- makeGRangesFromDataFrame(genebody.tb)\n\npeak.H3K27ac <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n\n\n# Step 1: Find overlaps between gene bodies and peaks\noverlaps <- findOverlaps(genebody.gr, peak.H3K27ac)\n\n# Create a list mapping each gene to the indices of peaks overlapping with its gene body\noverlapping_peaks_per_gene <- split(subjectHits(overlaps), queryHits(overlaps))\n\n# Initialize an empty list to store results\nresults_list <- vector(\\list\\, length(genes.gr))\n\n# Step 2: For each gene, exclude overlapping peaks and find the nearest peak to its TSS\nfor (i in seq_along(genes.gr)) {\n  gene <- genes.gr[i]\n  \n  # Get indices of peaks overlapping with this gene's body\n  overlapping_peak_indices <- overlapping_peaks_per_gene[[as.character(i)]]\n  \n  # Exclude overlapping peaks for this gene\n  if (!is.null(overlapping_peak_indices)) {\n    peaks_to_consider <- peak.H3K27ac[-overlapping_peak_indices]\n  } else {\n    peaks_to_consider <- peak.H3K27ac\n  }\n  \n  # Find the nearest peak to the TSS of this gene\n  nearest_peak_index <- nearest(gene, peaks_to_consider)\n  \n  if (is.na(nearest_peak_index)) {\n    # No peaks found; set NA values\n    results_list[[i]] <- data.frame(\n      ensembl = mcols(gene)$ensembl,\n      gene_chr = as.character(seqnames(gene)),\n      gene_TSS = start(gene),\n      peak_chr = NA,\n      peak_start = NA,\n      peak_end = NA,\n      distance = NA\n    )\n  } else {\n    nearest_peak <- peaks_to_consider[nearest_peak_index]\n    dist <- distance(gene, nearest_peak)\n    \n    results_list[[i]] <- data.frame(\n      ensembl = mcols(gene)$ensembl,\n      gene_chr = as.character(seqnames(gene)),\n      gene_TSS = start(gene),\n      peak_chr = as.character(seqnames(nearest_peak)),\n      peak_start = start(nearest_peak),\n      peak_end = end(nearest_peak),\n      distance = dist\n    )\n  }\n}\n\n# Combine results into a single data frame\nresults <- do.call(rbind, results_list)\n\n# Add group information\nresults <- results %>% dplyr::mutate(group = case_when(\n  ensembl %in% group1 ~ \\group1\\,\n  ensembl %in% group2 ~ \\group2\\,\n  TRUE ~ NA_character_\n))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n  distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetPvalWilcox(results, \\group1\\, \\group2\\)\n# Plotting\nggplot(results, aes(x = group, y = distance, fill = group)) +\n  geom_violin() +\n  geom_boxplot(outlier.shape = NA, width = 0.1, fill = \\white\\) +\n  theme_bw() +\n  ggtitle(\\Distance to Nearest H3K27ac Peak Excluding Gene Body Overlaps\\) +\n  scale_y_continuous(labels = label_kb_mb) +\n  coord_cartesian(ylim = c(0, 50 * 1000)) +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) \n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS) %>% dplyr::filter(ensembl %in% c(group1, group2))
genes.gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),
ensembl = gene.tb$ensembl
)
genebody.tb <- gene.tb %>% dplyr::mutate(start = TSS-10*1000, end = TSS + 10*1000) %>%
dplyr::select(chr, start, end)
genebody.gr <- makeGRangesFromDataFrame(genebody.tb)
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
# Step 1: Find overlaps between gene bodies and peaks
overlaps <- findOverlaps(genebody.gr, peak.H3K27ac)
# Create a list mapping each gene to the indices of peaks overlapping with its gene body
overlapping_peaks_per_gene <- split(subjectHits(overlaps), queryHits(overlaps))
# Initialize an empty list to store results
results_list <- vector(\list\, length(genes.gr))
# Step 2: For each gene, exclude overlapping peaks and find the nearest peak to its TSS
for (i in seq_along(genes.gr)) {
gene <- genes.gr[i]
# Get indices of peaks overlapping with this gene's body
overlapping_peak_indices <- overlapping_peaks_per_gene[[as.character(i)]]
# Exclude overlapping peaks for this gene
if (!is.null(overlapping_peak_indices)) {
peaks_to_consider <- peak.H3K27ac[-overlapping_peak_indices]
} else {
peaks_to_consider <- peak.H3K27ac
}
# Find the nearest peak to the TSS of this gene
nearest_peak_index <- nearest(gene, peaks_to_consider)
if (is.na(nearest_peak_index)) {
# No peaks found; set NA values
results_list[[i]] <- data.frame(
ensembl = mcols(gene)$ensembl,
gene_chr = as.character(seqnames(gene)),
gene_TSS = start(gene),
peak_chr = NA,
peak_start = NA,
peak_end = NA,
distance = NA
)
} else {
nearest_peak <- peaks_to_consider[nearest_peak_index]
dist <- distance(gene, nearest_peak)
results_list[[i]] <- data.frame(
ensembl = mcols(gene)$ensembl,
gene_chr = as.character(seqnames(gene)),
gene_TSS = start(gene),
peak_chr = as.character(seqnames(nearest_peak)),
peak_start = start(nearest_peak),
peak_end = end(nearest_peak),
distance = dist
)
}
}
# Combine results into a single data frame
results <- do.call(rbind, results_list)
# Add group information
results <- results %>% dplyr::mutate(group = case_when(
ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA_character_
))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$distance
distance2 <- (data %>% dplyr::filter(group == group2) )$distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
getPvalWilcox(results, \group1\, \group2\)
# Plotting
ggplot(results, aes(x = group, y = distance, fill = group)) +
geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) +
theme_bw() +
ggtitle(\Distance to Nearest H3K27ac Peak Excluding Gene Body Overlaps\) +
scale_y_continuous(labels = label_kb_mb) +
coord_cartesian(ylim = c(0, 50 * 1000)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3), ensembl = V6, chr = V1) %>%\n  dplyr::select(ensembl, chr, TSS) %>% dplyr::filter(ensembl %in% c(group1, group2))\ngenes.gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),\n  ensembl = gene.tb$ensembl\n)\n\ngenebody.tb <- gene.tb %>% dplyr::mutate(start = TSS-10*1000, end = TSS + 10*1000) %>%\n  dplyr::select(chr, start, end)\ngenebody.gr <- makeGRangesFromDataFrame(genebody.tb)\n\npeak.H3K27ac <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n\n\n# Step 1: Find overlaps between gene bodies and peaks\noverlaps <- findOverlaps(genebody.gr, peak.H3K27ac)\n\n# Create a list mapping each gene to the indices of peaks overlapping with its gene body\noverlapping_peaks_per_gene <- split(subjectHits(overlaps), queryHits(overlaps))\n\n# Initialize an empty list to store results\nresults_list <- vector(\\list\\, length(genes.gr))\n\n# Step 2: For each gene, exclude overlapping peaks and find the nearest peak to its TSS\nfor (i in seq_along(genes.gr)) {\n  gene <- genes.gr[i]\n  \n  # Get indices of peaks overlapping with this gene's body\n  overlapping_peak_indices <- overlapping_peaks_per_gene[[as.character(i)]]\n  \n  # Exclude overlapping peaks for this gene\n  if (!is.null(overlapping_peak_indices)) {\n    peaks_to_consider <- peak.H3K27ac[-overlapping_peak_indices]\n  } else {\n    peaks_to_consider <- peak.H3K27ac\n  }\n  \n  # Find the nearest peak to the TSS of this gene\n  nearest_peak_index <- nearest(gene, peaks_to_consider)\n  \n  if (is.na(nearest_peak_index)) {\n    # No peaks found; set NA values\n    results_list[[i]] <- data.frame(\n      ensembl = mcols(gene)$ensembl,\n      gene_chr = as.character(seqnames(gene)),\n      gene_TSS = start(gene),\n      peak_chr = NA,\n      peak_start = NA,\n      peak_end = NA,\n      distance = NA\n    )\n  } else {\n    nearest_peak <- peaks_to_consider[nearest_peak_index]\n    dist <- distance(gene, nearest_peak)\n    \n    results_list[[i]] <- data.frame(\n      ensembl = mcols(gene)$ensembl,\n      gene_chr = as.character(seqnames(gene)),\n      gene_TSS = start(gene),\n      peak_chr = as.character(seqnames(nearest_peak)),\n      peak_start = start(nearest_peak),\n      peak_end = end(nearest_peak),\n      distance = dist\n    )\n  }\n}\n\n# Combine results into a single data frame\nresults <- do.call(rbind, results_list)\n\n# Add group information\nresults <- results %>% dplyr::mutate(group = case_when(\n  ensembl %in% group1 ~ \\group1\\,\n  ensembl %in% group2 ~ \\group2\\,\n  TRUE ~ NA_character_\n))\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group == group1) )$distance\n  distance2 <- (data %>% dplyr::filter(group == group2) )$distance\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetPvalWilcox(results, \\group1\\, \\group2\\)\n# Plotting\nggplot(results, aes(x = group, y = distance, fill = group)) +\n  geom_violin() +\n  geom_boxplot(outlier.shape = NA, width = 0.1, fill = \\white\\) +\n  theme_bw() +\n  ggtitle(\\Distance to Nearest H3K27ac Peak Excluding Gene Body Overlaps\\) +\n  scale_y_continuous(labels = label_kb_mb) +\n  coord_cartesian(ylim = c(0, 50 * 1000)) +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) \n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS) %>% dplyr::filter(ensembl %in% c(group1, group2))
genes.gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$TSS, end = gene.tb$TSS),
ensembl = gene.tb$ensembl
)
genebody.tb <- gene.tb %>% dplyr::mutate(start = TSS-10*1000, end = TSS + 10*1000) %>%
dplyr::select(chr, start, end)
genebody.gr <- makeGRangesFromDataFrame(genebody.tb)
peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
# Step 1: Find overlaps between gene bodies and peaks
overlaps <- findOverlaps(genebody.gr, peak.H3K27ac)
# Create a list mapping each gene to the indices of peaks overlapping with its gene body
overlapping_peaks_per_gene <- split(subjectHits(overlaps), queryHits(overlaps))
# Initialize an empty list to store results
results_list <- vector(\list\, length(genes.gr))
# Step 2: For each gene, exclude overlapping peaks and find the nearest peak to its TSS
for (i in seq_along(genes.gr)) {
gene <- genes.gr[i]
# Get indices of peaks overlapping with this gene's body
overlapping_peak_indices <- overlapping_peaks_per_gene[[as.character(i)]]
# Exclude overlapping peaks for this gene
if (!is.null(overlapping_peak_indices)) {
peaks_to_consider <- peak.H3K27ac[-overlapping_peak_indices]
} else {
peaks_to_consider <- peak.H3K27ac
}
# Find the nearest peak to the TSS of this gene
nearest_peak_index <- nearest(gene, peaks_to_consider)
if (is.na(nearest_peak_index)) {
# No peaks found; set NA values
results_list[[i]] <- data.frame(
ensembl = mcols(gene)$ensembl,
gene_chr = as.character(seqnames(gene)),
gene_TSS = start(gene),
peak_chr = NA,
peak_start = NA,
peak_end = NA,
distance = NA
)
} else {
nearest_peak <- peaks_to_consider[nearest_peak_index]
dist <- distance(gene, nearest_peak)
results_list[[i]] <- data.frame(
ensembl = mcols(gene)$ensembl,
gene_chr = as.character(seqnames(gene)),
gene_TSS = start(gene),
peak_chr = as.character(seqnames(nearest_peak)),
peak_start = start(nearest_peak),
peak_end = end(nearest_peak),
distance = dist
)
}
}
# Combine results into a single data frame
results <- do.call(rbind, results_list)
# Add group information
results <- results %>% dplyr::mutate(group = case_when(
ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA_character_
))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group == group1) )$distance
distance2 <- (data %>% dplyr::filter(group == group2) )$distance
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
getPvalWilcox(results, \group1\, \group2\)
# Plotting
ggplot(results, aes(x = group, y = distance, fill = group)) +
geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) +
theme_bw() +
ggtitle(\Distance to Nearest H3K27ac Peak Excluding Gene Body Overlaps\) +
scale_y_continuous(labels = label_kb_mb) +
coord_cartesian(ylim = c(0, 50 * 1000)) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### binary group - excluding all TSS +- 10kb
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2) %>%
dplyr::filter(Anno2 == \P-E\)
temp <- geneAnnoData %>% unnest(gene)
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS)
temp <- dplyr::left_join(temp, gene.tb, by = c(\gene\ = \ensembl\))
temp <- temp %>% dplyr::mutate(center1 = (start1 + end1)/2,
center2 = (start2 + end2)/2,
distance1 = abs(TSS-center1),
distance2 = abs(TSS-center2)) %>%
dplyr::mutate(distance = pmax(distance1, distance2))
temp <- temp %>% group_by(gene) %>% summarize(min_enh_distance = min(distance)) %>%
dplyr::mutate(group = case_when(gene %in% group1 ~ \group1\,
gene %in% group2 ~ \group2\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(temp, aes(x = group, y = min_enh_distance, fill = group)) +
geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) +
theme_bw() +
ggtitle(\Distance to Nearest Enhancer fro E-P\) +
scale_y_continuous(labels = label_kb_mb) +
coord_cartesian(ylim = c(0, 1000 * 1000))
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2) %>%
dplyr::filter(Anno2 == \P-E\)
temp <- geneAnnoData %>% unnest(gene)
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3), ensembl = V6, chr = V1) %>%
dplyr::select(ensembl, chr, TSS)
temp <- dplyr::left_join(temp, gene.tb, by = c(\gene\ = \ensembl\))
temp <- temp %>% dplyr::mutate(center1 = (start1 + end1)/2,
center2 = (start2 + end2)/2,
distance1 = abs(TSS-center1),
distance2 = abs(TSS-center2)) %>%
dplyr::mutate(distance = pmax(distance1, distance2))
temp <- temp %>% group_by(gene) %>% summarize(min_enh_distance = min(distance)) %>%
dplyr::mutate(group = case_when(gene %in% group1 ~ \group1\,
gene %in% group2 ~ \group2\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(temp, aes(x = group, y = min_enh_distance, fill = group)) +
geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) +
theme_bw() +
ggtitle(\Distance to Nearest Enhancer fro E-P\) +
scale_y_continuous(labels = label_kb_mb) +
coord_cartesian(ylim = c(0, 1000 * 1000))
```
```
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##### binary group - excluding specific TSS +- 10 kb
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V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\n\ngeneAnnoData <- geneAnnoData %>% dplyr::filter(id %in% c(loop.up$id, loop.no$id, loop.down$id)) %>%\n  dplyr::mutate(loopType = case_when(id %in% c(loop.up$id, loop.no$id) ~ \\insensitive\\,\n                                     id %in% c(loop.down$id) ~ \\sensitive\\,\n                                     TRUE ~ NA))\n\ndata <- geneAnnoData %>% dplyr::select(gene, id, loopType) %>% unnest(gene) %>%\n  group_by(gene) %>% summarize(\n    insensitive = sum(loopType == \\insensitive\\, na.rm = TRUE),\n    sensitive = sum(loopType == \\sensitive\\, na.rm = TRUE))\n\n\n\n###\n\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ngeneGroup <- tibble(group = c(rep(\\group1\\, length(group1)), rep(\\group2\\, length(group2))),\n                    gene = c(group1, group2))\n\ndata <- left_join(geneGroup, data, by = c(\\gene\\)) %>% dplyr::mutate(mutate(across(everything(), ~replace_na(., 0))))\n\n\n###\ndata <- data %>% dplyr::mutate(presenceOfRetained = if_else(insensitive > 0, \\YES\\, \\NO\\),\n                       percOfRetained = if_else(insensitive + sensitive == 0, 0, 100*insensitive/(insensitive + sensitive)))\n\n\n###\nggplot(data, aes(x = group, y = percOfRetained, fill = group)) + geom_violin() + \n  geom_boxplot(outlier.shape = NA, width = 0.1, fill = \\white\\) + theme_bw()\n\n\n### Stacked barplot\ngroup <- c(\\group1\\, \\group1\\, \\group2\\, \\group2\\)\npresenceOfRetained <- rep(c(\\YES\\, \\NO\\), 2)\npresenceOfRetained <- factor (presenceOfRetained, levels = c(\\YES\\, \\NO\\))\nvalue <- c(nrow(data %>% dplyr::filter(group == \\group1\\, presenceOfRetained == \\YES\\)),\n           nrow(data %>% dplyr::filter(group == \\group1\\, presenceOfRetained == \\NO\\)),\n           nrow(data %>% dplyr::filter(group == \\group2\\, presenceOfRetained == \\YES\\)),\n           nrow(data %>% dplyr::filter(group == \\group2\\, presenceOfRetained == \\NO\\)))\nplotData <- data.frame(group, presenceOfRetained, value)\n\nggplot(plotData, aes(fill=presenceOfRetained, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
geneAnnoData <- geneAnnoData %>% dplyr::filter(id %in% c(loop.up$id, loop.no$id, loop.down$id)) %>%
dplyr::mutate(loopType = case_when(id %in% c(loop.up$id, loop.no$id) ~ \insensitive\,
id %in% c(loop.down$id) ~ \sensitive\,
TRUE ~ NA))
data <- geneAnnoData %>% dplyr::select(gene, id, loopType) %>% unnest(gene) %>%
group_by(gene) %>% summarize(
insensitive = sum(loopType == \insensitive\, na.rm = TRUE),
sensitive = sum(loopType == \sensitive\, na.rm = TRUE))
###
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
geneGroup <- tibble(group = c(rep(\group1\, length(group1)), rep(\group2\, length(group2))),
gene = c(group1, group2))
data <- left_join(geneGroup, data, by = c(\gene\)) %>% dplyr::mutate(mutate(across(everything(), ~replace_na(., 0))))
###
data <- data %>% dplyr::mutate(presenceOfRetained = if_else(insensitive > 0, \YES\, \NO\),
percOfRetained = if_else(insensitive + sensitive == 0, 0, 100*insensitive/(insensitive + sensitive)))
###
ggplot(data, aes(x = group, y = percOfRetained, fill = group)) + geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) + theme_bw()
### Stacked barplot
group <- c(\group1\, \group1\, \group2\, \group2\)
presenceOfRetained <- rep(c(\YES\, \NO\), 2)
presenceOfRetained <- factor (presenceOfRetained, levels = c(\YES\, \NO\))
value <- c(nrow(data %>% dplyr::filter(group == \group1\, presenceOfRetained == \YES\)),
nrow(data %>% dplyr::filter(group == \group1\, presenceOfRetained == \NO\)),
nrow(data %>% dplyr::filter(group == \group2\, presenceOfRetained == \YES\)),
nrow(data %>% dplyr::filter(group == \group2\, presenceOfRetained == \NO\)))
plotData <- data.frame(group, presenceOfRetained, value)
ggplot(plotData, aes(fill=presenceOfRetained, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                 diffCutoff = 0.2)\n\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\n\ngeneAnnoData <- geneAnnoData %>% dplyr::filter(id %in% c(loop.up$id, loop.no$id, loop.down$id)) %>%\n  dplyr::mutate(loopType = case_when(id %in% c(loop.up$id, loop.no$id) ~ \\insensitive\\,\n                                     id %in% c(loop.down$id) ~ \\sensitive\\,\n                                     TRUE ~ NA))\n\ndata <- geneAnnoData %>% dplyr::select(gene, id, loopType) %>% unnest(gene) %>%\n  group_by(gene) %>% summarize(\n    insensitive = sum(loopType == \\insensitive\\, na.rm = TRUE),\n    sensitive = sum(loopType == \\sensitive\\, na.rm = TRUE))\n\n\n\n###\n\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ngeneGroup <- tibble(group = c(rep(\\group1\\, length(group1)), rep(\\group2\\, length(group2))),\n                    gene = c(group1, group2))\n\ndata <- left_join(geneGroup, data, by = c(\\gene\\)) %>% dplyr::mutate(mutate(across(everything(), ~replace_na(., 0))))\n\n\n###\ndata <- data %>% dplyr::mutate(presenceOfRetained = if_else(insensitive > 0, \\YES\\, \\NO\\),\n                       percOfRetained = if_else(insensitive + sensitive == 0, 0, 100*insensitive/(insensitive + sensitive)))\n\n\n###\nggplot(data, aes(x = group, y = percOfRetained, fill = group)) + geom_violin() + \n  geom_boxplot(outlier.shape = NA, width = 0.1, fill = \\white\\) + theme_bw()\n\n\n### Stacked barplot\ngroup <- c(\\group1\\, \\group1\\, \\group2\\, \\group2\\)\npresenceOfRetained <- rep(c(\\YES\\, \\NO\\), 2)\npresenceOfRetained <- factor (presenceOfRetained, levels = c(\\YES\\, \\NO\\))\nvalue <- c(nrow(data %>% dplyr::filter(group == \\group1\\, presenceOfRetained == \\YES\\)),\n           nrow(data %>% dplyr::filter(group == \\group1\\, presenceOfRetained == \\NO\\)),\n           nrow(data %>% dplyr::filter(group == \\group2\\, presenceOfRetained == \\YES\\)),\n           nrow(data %>% dplyr::filter(group == \\group2\\, presenceOfRetained == \\NO\\)))\nplotData <- data.frame(group, presenceOfRetained, value)\n\nggplot(plotData, aes(fill=presenceOfRetained, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
geneAnnoData <- geneAnnoData %>% dplyr::filter(id %in% c(loop.up$id, loop.no$id, loop.down$id)) %>%
dplyr::mutate(loopType = case_when(id %in% c(loop.up$id, loop.no$id) ~ \insensitive\,
id %in% c(loop.down$id) ~ \sensitive\,
TRUE ~ NA))
data <- geneAnnoData %>% dplyr::select(gene, id, loopType) %>% unnest(gene) %>%
group_by(gene) %>% summarize(
insensitive = sum(loopType == \insensitive\, na.rm = TRUE),
sensitive = sum(loopType == \sensitive\, na.rm = TRUE))
###
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
geneGroup <- tibble(group = c(rep(\group1\, length(group1)), rep(\group2\, length(group2))),
gene = c(group1, group2))
data <- left_join(geneGroup, data, by = c(\gene\)) %>% dplyr::mutate(mutate(across(everything(), ~replace_na(., 0))))
###
data <- data %>% dplyr::mutate(presenceOfRetained = if_else(insensitive > 0, \YES\, \NO\),
percOfRetained = if_else(insensitive + sensitive == 0, 0, 100*insensitive/(insensitive + sensitive)))
###
ggplot(data, aes(x = group, y = percOfRetained, fill = group)) + geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.1, fill = \white\) + theme_bw()
### Stacked barplot
group <- c(\group1\, \group1\, \group2\, \group2\)
presenceOfRetained <- rep(c(\YES\, \NO\), 2)
presenceOfRetained <- factor (presenceOfRetained, levels = c(\YES\, \NO\))
value <- c(nrow(data %>% dplyr::filter(group == \group1\, presenceOfRetained == \YES\)),
nrow(data %>% dplyr::filter(group == \group1\, presenceOfRetained == \NO\)),
nrow(data %>% dplyr::filter(group == \group2\, presenceOfRetained == \YES\)),
nrow(data %>% dplyr::filter(group == \group2\, presenceOfRetained == \NO\)))
plotData <- data.frame(group, presenceOfRetained, value)
ggplot(plotData, aes(fill=presenceOfRetained, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
```
```
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<!-- rnb-text-begin -->
##### Only limiting to E-P loops from Micro-C
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V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\n\ngeneAnnoData <- geneAnnoData %>% dplyr::filter(id %in% c(loop.up$id, loop.no$id, loop.down$id)) %>%\n  dplyr::mutate(loopType = case_when(id %in% c(loop.up$id, loop.no$id) ~ \\insensitive\\,\n                                     id %in% c(loop.down$id) ~ \\sensitive\\,\n                                     TRUE ~ NA))\n\ndata <- geneAnnoData %>% dplyr::select(gene, id, loopType) %>% unnest(gene) %>%\n  group_by(gene) %>% summarize(\n    insensitive = sum(loopType == \\insensitive\\, na.rm = TRUE),\n    sensitive = sum(loopType == \\sensitive\\, na.rm = TRUE))\n\n\n\n###\n\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ngeneGroup <- tibble(group = c(rep(\\group1\\, length(group1)), rep(\\group2\\, length(group2))),\n                    gene = c(group1, group2))\n\ndata <- left_join(geneGroup, data, by = c(\\gene\\)) %>% dplyr::mutate(mutate(across(everything(), ~replace_na(., 0))))\n\n\n###\ndata <- data %>% dplyr::mutate(presenceOfRetained = if_else(insensitive > 0, \\YES\\, \\NO\\),\n                       percOfRetained = if_else(insensitive + sensitive == 0, 0, 100*insensitive/(insensitive + sensitive)))\n\n\n###\nggplot(data, aes(x = group, y = percOfRetained, fill = group)) + geom_violin() + \n  geom_boxplot(outlier.shape = NA, width = 0.05, fill = \\white\\) + theme_bw()\n\n\n### Stacked barplot\ngroup <- c(\\group1\\, \\group1\\, \\group2\\, \\group2\\)\npresenceOfRetained <- rep(c(\\YES\\, \\NO\\), 2)\npresenceOfRetained <- factor (presenceOfRetained, levels = c(\\YES\\, \\NO\\))\nvalue <- c(nrow(data %>% dplyr::filter(group == \\group1\\, presenceOfRetained == \\YES\\)),\n           nrow(data %>% dplyr::filter(group == \\group1\\, presenceOfRetained == \\NO\\)),\n           nrow(data %>% dplyr::filter(group == \\group2\\, presenceOfRetained == \\YES\\)),\n           nrow(data %>% dplyr::filter(group == \\group2\\, presenceOfRetained == \\NO\\)))\nplotData <- data.frame(group, presenceOfRetained, value)\n\nggplot(plotData, aes(fill=presenceOfRetained, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
geneAnnoData <- geneAnnoData %>% dplyr::filter(id %in% c(loop.up$id, loop.no$id, loop.down$id)) %>%
dplyr::mutate(loopType = case_when(id %in% c(loop.up$id, loop.no$id) ~ \insensitive\,
id %in% c(loop.down$id) ~ \sensitive\,
TRUE ~ NA))
data <- geneAnnoData %>% dplyr::select(gene, id, loopType) %>% unnest(gene) %>%
group_by(gene) %>% summarize(
insensitive = sum(loopType == \insensitive\, na.rm = TRUE),
sensitive = sum(loopType == \sensitive\, na.rm = TRUE))
###
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
geneGroup <- tibble(group = c(rep(\group1\, length(group1)), rep(\group2\, length(group2))),
gene = c(group1, group2))
data <- left_join(geneGroup, data, by = c(\gene\)) %>% dplyr::mutate(mutate(across(everything(), ~replace_na(., 0))))
###
data <- data %>% dplyr::mutate(presenceOfRetained = if_else(insensitive > 0, \YES\, \NO\),
percOfRetained = if_else(insensitive + sensitive == 0, 0, 100*insensitive/(insensitive + sensitive)))
###
ggplot(data, aes(x = group, y = percOfRetained, fill = group)) + geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.05, fill = \white\) + theme_bw()
### Stacked barplot
group <- c(\group1\, \group1\, \group2\, \group2\)
presenceOfRetained <- rep(c(\YES\, \NO\), 2)
presenceOfRetained <- factor (presenceOfRetained, levels = c(\YES\, \NO\))
value <- c(nrow(data %>% dplyr::filter(group == \group1\, presenceOfRetained == \YES\)),
nrow(data %>% dplyr::filter(group == \group1\, presenceOfRetained == \NO\)),
nrow(data %>% dplyr::filter(group == \group2\, presenceOfRetained == \YES\)),
nrow(data %>% dplyr::filter(group == \group2\, presenceOfRetained == \NO\)))
plotData <- data.frame(group, presenceOfRetained, value)
ggplot(plotData, aes(fill=presenceOfRetained, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-pe_ensemblList.tsv\\)),\n                                 diffCutoff = 0.2)\n\n\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(size = V3 - V2,\n                id = paste(V1, size, V2, V5, sep = \\_\\))\n\ngeneAnnoData <- geneAnnoData %>% dplyr::filter(id %in% c(loop.up$id, loop.no$id, loop.down$id)) %>%\n  dplyr::mutate(loopType = case_when(id %in% c(loop.up$id, loop.no$id) ~ \\insensitive\\,\n                                     id %in% c(loop.down$id) ~ \\sensitive\\,\n                                     TRUE ~ NA))\n\ndata <- geneAnnoData %>% dplyr::select(gene, id, loopType) %>% unnest(gene) %>%\n  group_by(gene) %>% summarize(\n    insensitive = sum(loopType == \\insensitive\\, na.rm = TRUE),\n    sensitive = sum(loopType == \\sensitive\\, na.rm = TRUE))\n\n\n\n###\n\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ngeneGroup <- tibble(group = c(rep(\\group1\\, length(group1)), rep(\\group2\\, length(group2))),\n                    gene = c(group1, group2))\n\ndata <- left_join(geneGroup, data, by = c(\\gene\\)) %>% dplyr::mutate(mutate(across(everything(), ~replace_na(., 0))))\n\n\n###\ndata <- data %>% dplyr::mutate(presenceOfRetained = if_else(insensitive > 0, \\YES\\, \\NO\\),\n                       percOfRetained = if_else(insensitive + sensitive == 0, 0, 100*insensitive/(insensitive + sensitive)))\n\n\n###\nggplot(data, aes(x = group, y = percOfRetained, fill = group)) + geom_violin() + \n  geom_boxplot(outlier.shape = NA, width = 0.05, fill = \\white\\) + theme_bw()\n\n\n### Stacked barplot\ngroup <- c(\\group1\\, \\group1\\, \\group2\\, \\group2\\)\npresenceOfRetained <- rep(c(\\YES\\, \\NO\\), 2)\npresenceOfRetained <- factor (presenceOfRetained, levels = c(\\YES\\, \\NO\\))\nvalue <- c(nrow(data %>% dplyr::filter(group == \\group1\\, presenceOfRetained == \\YES\\)),\n           nrow(data %>% dplyr::filter(group == \\group1\\, presenceOfRetained == \\NO\\)),\n           nrow(data %>% dplyr::filter(group == \\group2\\, presenceOfRetained == \\YES\\)),\n           nrow(data %>% dplyr::filter(group == \\group2\\, presenceOfRetained == \\NO\\)))\nplotData <- data.frame(group, presenceOfRetained, value)\n\nggplot(plotData, aes(fill=presenceOfRetained, y=value, x=group)) + \n    geom_bar(position=\\fill\\, stat=\\identity\\) + theme_classic()\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-pe_ensemblList.tsv\)),
diffCutoff = 0.2)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(size = V3 - V2,
id = paste(V1, size, V2, V5, sep = \_\))
geneAnnoData <- geneAnnoData %>% dplyr::filter(id %in% c(loop.up$id, loop.no$id, loop.down$id)) %>%
dplyr::mutate(loopType = case_when(id %in% c(loop.up$id, loop.no$id) ~ \insensitive\,
id %in% c(loop.down$id) ~ \sensitive\,
TRUE ~ NA))
data <- geneAnnoData %>% dplyr::select(gene, id, loopType) %>% unnest(gene) %>%
group_by(gene) %>% summarize(
insensitive = sum(loopType == \insensitive\, na.rm = TRUE),
sensitive = sum(loopType == \sensitive\, na.rm = TRUE))
###
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
geneGroup <- tibble(group = c(rep(\group1\, length(group1)), rep(\group2\, length(group2))),
gene = c(group1, group2))
data <- left_join(geneGroup, data, by = c(\gene\)) %>% dplyr::mutate(mutate(across(everything(), ~replace_na(., 0))))
###
data <- data %>% dplyr::mutate(presenceOfRetained = if_else(insensitive > 0, \YES\, \NO\),
percOfRetained = if_else(insensitive + sensitive == 0, 0, 100*insensitive/(insensitive + sensitive)))
###
ggplot(data, aes(x = group, y = percOfRetained, fill = group)) + geom_violin() +
geom_boxplot(outlier.shape = NA, width = 0.05, fill = \white\) + theme_bw()
### Stacked barplot
group <- c(\group1\, \group1\, \group2\, \group2\)
presenceOfRetained <- rep(c(\YES\, \NO\), 2)
presenceOfRetained <- factor (presenceOfRetained, levels = c(\YES\, \NO\))
value <- c(nrow(data %>% dplyr::filter(group == \group1\, presenceOfRetained == \YES\)),
nrow(data %>% dplyr::filter(group == \group1\, presenceOfRetained == \NO\)),
nrow(data %>% dplyr::filter(group == \group2\, presenceOfRetained == \YES\)),
nrow(data %>% dplyr::filter(group == \group2\, presenceOfRetained == \NO\)))
plotData <- data.frame(group, presenceOfRetained, value)
ggplot(plotData, aes(fill=presenceOfRetained, y=value, x=group)) +
geom_bar(position=\fill\, stat=\identity\) + theme_classic()
```
```
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### [2.24] (WORKING ON)Checking the percentage of non-important loops
Idea: having one non-perturbed reg loops could be enough.
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nloops <- fread(here(consensusDir, \\chromo_cons_score.tsv\\))\n\nsample <- \\G1.DMSO.Merged\\\nfor(sample in c(\\G1.DMSO.Merged\\, \\G1.dTAG.Merged\\, \\G1.A485.Merged\\)){\n  temp.25kb <- fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_25kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.25kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp.10kb <- fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_10kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.10kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp.5kb <-fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_5kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.5kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)\n  fwrite(temp, here(consensusDir, paste0(\\loopScore_\\, sample, \\.tsv\\)), sep = \\\\t\\)\n}\n\n\ntemp.DMSO <- fread(here(consensusDir, \\loopScore_G1.DMSO.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.DMSO) <- c(\\id\\, \\obs_DMSO\\, \\obsexp_DMSO\\)\ntemp.dTAG <- fread(here(consensusDir, \\loopScore_G1.dTAG.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.dTAG) <- c(\\id\\, \\obs_dTAG\\, \\obsexp_dTAG\\)\ntemp.A485 <- fread(here(consensusDir, \\loopScore_G1.A485.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.A485) <- c(\\id\\, \\obs_A485\\, \\obsexp_A485\\)\n\nloops_oe <- full_join(full_join(temp.DMSO, temp.dTAG, by = \\id\\),\n          temp.A485, by = \\id\\) %>%\n  dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),\n                oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))\n\nfwrite(loops_oe, here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\)), sep = \\\\t\\)\n```\n\n<!-- rnb-source-end -->\n"} -->
loops <- fread(here(consensusDir, \chromo_cons_score.tsv\))
sample <- \G1.DMSO.Merged\
for(sample in c(\G1.DMSO.Merged\, \G1.dTAG.Merged\, \G1.A485.Merged\)){
temp.25kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_25kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.25kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.10kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_10kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.10kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.5kb <-fread(here(consensusDir, paste0(\loopScore_\, sample, \_5kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.5kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)
fwrite(temp, here(consensusDir, paste0(\loopScore_\, sample, \.tsv\)), sep = \\t\)
}
temp.DMSO <- fread(here(consensusDir, \loopScore_G1.DMSO.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.DMSO) <- c(\id\, \obs_DMSO\, \obsexp_DMSO\)
temp.dTAG <- fread(here(consensusDir, \loopScore_G1.dTAG.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.dTAG) <- c(\id\, \obs_dTAG\, \obsexp_dTAG\)
temp.A485 <- fread(here(consensusDir, \loopScore_G1.A485.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.A485) <- c(\id\, \obs_A485\, \obsexp_A485\)
loops_oe <- full_join(full_join(temp.DMSO, temp.dTAG, by = \id\),
temp.A485, by = \id\) %>%
dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),
oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))
fwrite(loops_oe, here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\)), sep = \\t\)
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start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.25kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp.10kb <- fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_10kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.10kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp.5kb <-fread(here(consensusDir, paste0(\\loopScore_\\, sample, \\_5kb.tsv\\))) %>%\n    dplyr::mutate(binSize = end1 - start1,\n                  id = paste(chrom1, binSize, start1, start2, sep = \\_\\)) %>%\n    dplyr::filter(id %in% loops$id) %>%\n    dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \\O/E\\)\n  colnames(temp.5kb) <- c(\\chrom1\\, \\start1\\, \\end1\\, \\chrom2\\, \\start2\\, \\end2\\, \\id\\, \\obs\\, \\obsexp\\)\n  \n  temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)\n  fwrite(temp, here(consensusDir, paste0(\\loopScore_\\, sample, \\.tsv\\)), sep = \\\\t\\)\n}\n\n\ntemp.DMSO <- fread(here(consensusDir, \\loopScore_G1.DMSO.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.DMSO) <- c(\\id\\, \\obs_DMSO\\, \\obsexp_DMSO\\)\ntemp.dTAG <- fread(here(consensusDir, \\loopScore_G1.dTAG.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.dTAG) <- c(\\id\\, \\obs_dTAG\\, \\obsexp_dTAG\\)\ntemp.A485 <- fread(here(consensusDir, \\loopScore_G1.A485.Merged.tsv\\)) %>%\n  dplyr::select(id, obs, obsexp)\ncolnames(temp.A485) <- c(\\id\\, \\obs_A485\\, \\obsexp_A485\\)\n\nloops_oe <- full_join(full_join(temp.DMSO, temp.dTAG, by = \\id\\),\n          temp.A485, by = \\id\\) %>%\n  dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),\n                oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))\n\nfwrite(loops_oe, here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\)), sep = \\\\t\\)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
loops <- fread(here(consensusDir, \chromo_cons_score.tsv\))
sample <- \G1.DMSO.Merged\
for(sample in c(\G1.DMSO.Merged\, \G1.dTAG.Merged\, \G1.A485.Merged\)){
temp.25kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_25kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.25kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.10kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_10kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.10kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.5kb <-fread(here(consensusDir, paste0(\loopScore_\, sample, \_5kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.5kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)
fwrite(temp, here(consensusDir, paste0(\loopScore_\, sample, \.tsv\)), sep = \\t\)
}
temp.DMSO <- fread(here(consensusDir, \loopScore_G1.DMSO.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.DMSO) <- c(\id\, \obs_DMSO\, \obsexp_DMSO\)
temp.dTAG <- fread(here(consensusDir, \loopScore_G1.dTAG.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.dTAG) <- c(\id\, \obs_dTAG\, \obsexp_dTAG\)
temp.A485 <- fread(here(consensusDir, \loopScore_G1.A485.Merged.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.A485) <- c(\id\, \obs_A485\, \obsexp_A485\)
loops_oe <- full_join(full_join(temp.DMSO, temp.dTAG, by = \id\),
temp.A485, by = \id\) %>%
dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),
oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))
fwrite(loops_oe, here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\)), sep = \\t\)
```
```
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loops <- fread(here(consensusDir, \chromo_cons_score.tsv\))
for(sample in c(\Async.UT\, \Async.AID\)){
temp.25kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_25kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.25kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.10kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_10kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.10kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.5kb <-fread(here(consensusDir, paste0(\loopScore_\, sample, \_5kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.5kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)
fwrite(temp, here(consensusDir, paste0(\loopScore_\, sample, \.tsv\)), sep = \\t\)
}
temp.UT <- fread(here(consensusDir, \loopScore_Async.UT.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.UT) <- c(\id\, \obs_UT\, \obsexp_UT\)
temp.AID <- fread(here(consensusDir, \loopScore_Async.AID.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.AID) <- c(\id\, \obs_AID\, \obsexp_AID\)
loops_oe <- full_join(temp.UT, temp.AID, by = \id\) %>%
dplyr::mutate(oeFC_AID_UT = if_else(obsexp_UT == 0, NA, obsexp_AID/obsexp_UT))
fwrite(loops_oe, here(consensusDir, paste0(\loopScore_cons_obsexp_Async.tsv\)), sep = \\t\)
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-->
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```r
```r
loops <- fread(here(consensusDir, \chromo_cons_score.tsv\))
for(sample in c(\Async.UT\, \Async.AID\)){
temp.25kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_25kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.25kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.10kb <- fread(here(consensusDir, paste0(\loopScore_\, sample, \_10kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.10kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp.5kb <-fread(here(consensusDir, paste0(\loopScore_\, sample, \_5kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(chrom1, start1, end1, chrom2, start2, end2, id, observed, \O/E\)
colnames(temp.5kb) <- c(\chrom1\, \start1\, \end1\, \chrom2\, \start2\, \end2\, \id\, \obs\, \obsexp\)
temp <- bind_rows(temp.25kb, temp.10kb, temp.5kb)
fwrite(temp, here(consensusDir, paste0(\loopScore_\, sample, \.tsv\)), sep = \\t\)
}
temp.UT <- fread(here(consensusDir, \loopScore_Async.UT.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.UT) <- c(\id\, \obs_UT\, \obsexp_UT\)
temp.AID <- fread(here(consensusDir, \loopScore_Async.AID.tsv\)) %>%
dplyr::select(id, obs, obsexp)
colnames(temp.AID) <- c(\id\, \obs_AID\, \obsexp_AID\)
loops_oe <- full_join(temp.UT, temp.AID, by = \id\) %>%
dplyr::mutate(oeFC_AID_UT = if_else(obsexp_UT == 0, NA, obsexp_AID/obsexp_UT))
fwrite(loops_oe, here(consensusDir, paste0(\loopScore_cons_obsexp_Async.tsv\)), sep = \\t\)
```
```
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### [2.25] Obs/Exp loops
#### Merging obs/exp scores from different resolution & samples
##### G1
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NDg1X0RNU08gPSBpZl9lbHNlKG9ic2V4cF9ETVNPID09IDAsIE5BLCBvYnNleHBfQTQ4NS9vYnNleHBfRE1TTykpXG4gIFxuICBmd3JpdGUodGVtcCwgaGVyZShjb25zZW5zdXNEaXIsIHBhc3RlMChcXGxvb3BTY29yZV9jb25zX29ic2V4cF9cXCwgcmVzLCBcXGtiLnRzdlxcKSksIHNlcCA9IFxcXFx0XFwpXG59XG5cblxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
loops <- fread(here(consensusDir, \chromo_cons_score.tsv\))
res <- 25
for (res in c(25, 10, 5)){
temp.DMSO <- fread(here(consensusDir, paste0(\loopScore_G1.DMSO.Merged_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.DMSO) <- c(\id\, \obs_DMSO\, \obsexp_DMSO\)
temp.dTAG <- fread(here(consensusDir, paste0(\loopScore_G1.dTAG.Merged_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.dTAG) <- c(\id\, \obs_dTAG\, \obsexp_dTAG\)
temp.A485 <- fread(here(consensusDir, paste0(\loopScore_G1.A485.Merged_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.A485) <- c(\id\, \obs_A485\, \obsexp_A485\)
temp <- full_join(full_join(temp.DMSO, temp.dTAG, by = c(\id\)), temp.A485, by = c(\id\)) %>%
dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),
oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))
fwrite(temp, here(consensusDir, paste0(\loopScore_cons_obsexp_\, res, \kb.tsv\)), sep = \\t\)
}
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```r
```r
loops <- fread(here(consensusDir, \chromo_cons_score.tsv\))
res <- 25
for (res in c(25, 10, 5)){
temp.DMSO <- fread(here(consensusDir, paste0(\loopScore_G1.DMSO.Merged_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.DMSO) <- c(\id\, \obs_DMSO\, \obsexp_DMSO\)
temp.dTAG <- fread(here(consensusDir, paste0(\loopScore_G1.dTAG.Merged_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.dTAG) <- c(\id\, \obs_dTAG\, \obsexp_dTAG\)
temp.A485 <- fread(here(consensusDir, paste0(\loopScore_G1.A485.Merged_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.A485) <- c(\id\, \obs_A485\, \obsexp_A485\)
temp <- full_join(full_join(temp.DMSO, temp.dTAG, by = c(\id\)), temp.A485, by = c(\id\)) %>%
dplyr::mutate(oeFC_dTAG_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_dTAG/obsexp_DMSO),
oeFC_A485_DMSO = if_else(obsexp_DMSO == 0, NA, obsexp_A485/obsexp_DMSO))
fwrite(temp, here(consensusDir, paste0(\loopScore_cons_obsexp_\, res, \kb.tsv\)), sep = \\t\)
}
```
```
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##### Async
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<!-- rnb-output-begin 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 -->
loops <- fread(here(consensusDir, \chromo_cons_score.tsv\))
res <- 25
for (res in c(25, 10, 5)){
temp.UT <- fread(here(consensusDir, paste0(\loopScore_Async.UT_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.UT) <- c(\id\, \obs_UT\, \obsexp_UT\)
temp.AID <- fread(here(consensusDir, paste0(\loopScore_Async.AID_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.AID) <- c(\id\, \obs_AID\, \obsexp_AID\)
temp <- full_join(temp.UT, temp.AID, by = c(\id\)) %>%
dplyr::mutate(oeFC_AID_UT = if_else(obsexp_UT == 0, NA, obsexp_AID/obsexp_UT))
fwrite(temp, here(consensusDir, paste0(\loopScore_cons_obsexp_\, res, \kb_async.tsv\)), sep = \\t\)
}
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
loops <- fread(here(consensusDir, \chromo_cons_score.tsv\))
res <- 25
for (res in c(25, 10, 5)){
temp.UT <- fread(here(consensusDir, paste0(\loopScore_Async.UT_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.UT) <- c(\id\, \obs_UT\, \obsexp_UT\)
temp.AID <- fread(here(consensusDir, paste0(\loopScore_Async.AID_\, res, \kb.tsv\))) %>%
dplyr::mutate(binSize = end1 - start1,
id = paste(chrom1, binSize, start1, start2, sep = \_\)) %>%
dplyr::filter(id %in% loops$id) %>%
dplyr::select(id, observed, \O/E\)
colnames(temp.AID) <- c(\id\, \obs_AID\, \obsexp_AID\)
temp <- full_join(temp.UT, temp.AID, by = c(\id\)) %>%
dplyr::mutate(oeFC_AID_UT = if_else(obsexp_UT == 0, NA, obsexp_AID/obsexp_UT))
fwrite(temp, here(consensusDir, paste0(\loopScore_cons_obsexp_\, res, \kb_async.tsv\)), sep = \\t\)
}
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Merging obs/exp per resolution
Only the loops that made to consensus loops are considered
##### G1
<!-- rnb-text-end -->
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-->\n\n```r\n# Importing loops\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\\)) %>% \n  dplyr::mutate(binSize = V3 - V2,\n                id = paste(V1, binSize, V2, V5, sep = \\_\\))\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>% \n  dplyr::mutate(binSize = V3 - V2,\n                id = paste(V1, binSize, V2, V5, sep = \\_\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>% \n  dplyr::mutate(binSize = V3 - V2,\n                id = paste(V1, binSize, V2, V5, sep = \\_\\))\n\nfor(res in c(25, 10, 5)){\n  minValue <- -4\n  maxValue <- 5\n  obsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp_\\, res, \\kb.tsv\\))) %>%\n    dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                  log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                  log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))\n  \n  \n  # dTAG\n  obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_dTAG, n = 100)\n  obsexp <- obsexp %>% dplyr::arrange(density)\n  g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) + \n    geom_point() + theme_bw() + scale_color_viridis(guide = \\none\\) +\n    geom_abline(slope = 1, intercept = 0) +\n    ggtitle(paste0(res, \\kb\n"} -->
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
for(res in c(25, 10, 5)){
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp_\, res, \kb.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# dTAG
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_dTAG, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(res, \kb```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
for(res in c(25, 10, 5)){
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp_\, res, \kb.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# dTAG
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_dTAG, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(res, \kb
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-->
```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# dTAG
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_dTAG, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obsexp_DMSO, temp$log_obsexp_dTAG, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# dTAG
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_dTAG, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obsexp_DMSO, temp$log_obsexp_dTAG, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)
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```r
specificLoop.25kb <- fread(here(loopDir, \G1.DMSO.Merged_chromosight_25kb.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
specificLoop.10kb <- fread(here(loopDir, \G1.DMSO.Merged_chromosight_10kb.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
specificLoop.5kb <- fread(here(loopDir, \G1.DMSO.Merged_chromosight_5kb.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
specificLoops.DMSO <- c(specificLoop.25kb$id, specificLoop.10kb$id, specificLoop.5kb$id)
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Structural
loop.str <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
temp <- obsexp %>% dplyr::filter(id %in% loop.str$id,
id %in% specificLoops.DMSO)
temp$density <- get_density(temp$log_obsexp_DMSO, temp$log_obsexp_dTAG, n = 100)
temp <- temp %>% dplyr::arrange(density)
g5 <- ggplot(temp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)```r
specificLoop.25kb <- fread(here(loopDir, \G1.DMSO.Merged_chromosight_25kb.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
specificLoop.10kb <- fread(here(loopDir, \G1.DMSO.Merged_chromosight_10kb.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
specificLoop.5kb <- fread(here(loopDir, \G1.DMSO.Merged_chromosight_5kb.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
specificLoops.DMSO <- c(specificLoop.25kb$id, specificLoop.10kb$id, specificLoop.5kb$id)
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Structural
loop.str <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
temp <- obsexp %>% dplyr::filter(id %in% loop.str$id,
id %in% specificLoops.DMSO)
temp$density <- get_density(temp$log_obsexp_DMSO, temp$log_obsexp_dTAG, n = 100)
temp <- temp %>% dplyr::arrange(density)
g5 <- ggplot(temp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)
<!-- rnb-source-begin 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-->
```r
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>% dplyr::mutate(distance = start2 - start1)
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
# distance filter
obsexp <- data %>% dplyr::filter(distance > 1e6)
# dTAG
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_dTAG, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obsexp_DMSO, temp$log_obsexp_dTAG, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)```r
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>% dplyr::mutate(distance = start2 - start1)
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
# distance filter
obsexp <- data %>% dplyr::filter(distance > 1e6)
# dTAG
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_dTAG, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obsexp_DMSO, temp$log_obsexp_dTAG, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)
<!-- rnb-source-begin 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-->
```r
minValue <- -4
maxValue <- 10
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obs_DMSO = if_else(obs_DMSO == 0, minValue, log2(obs_DMSO)),
log_obs_dTAG = if_else(obs_dTAG == 0, minValue, log2(obs_dTAG)),
log_obs_A485 = if_else(obs_A485 == 0, minValue, log2(obs_A485)))
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
# dTAG
obsexp$density <- get_density(obsexp$log_obs_DMSO, obsexp$log_obs_dTAG, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obs_DMSO, y = log_obs_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obs_DMSO, temp$log_obs_dTAG, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obs_DMSO, y = log_obs_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)```r
minValue <- -4
maxValue <- 10
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obs_DMSO = if_else(obs_DMSO == 0, minValue, log2(obs_DMSO)),
log_obs_dTAG = if_else(obs_dTAG == 0, minValue, log2(obs_dTAG)),
log_obs_A485 = if_else(obs_A485 == 0, minValue, log2(obs_A485)))
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
# dTAG
obsexp$density <- get_density(obsexp$log_obs_DMSO, obsexp$log_obs_dTAG, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obs_DMSO, y = log_obs_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obs_DMSO, temp$log_obs_dTAG, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obs_DMSO, y = log_obs_dTAG, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)
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-->
```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
minValue <- -8
maxValue <- 7
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp_Async.tsv\))) %>%
dplyr::mutate(log_obsexp_UT = if_else(obsexp_UT == 0, minValue, log2(obsexp_UT)),
log_obsexp_AID = if_else(obsexp_AID == 0, minValue, log2(obsexp_AID)))
# dTAG
obsexp$density <- get_density(obsexp$log_obsexp_UT, obsexp$log_obsexp_AID, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_UT, y = log_obsexp_AID, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obsexp_UT, temp$log_obsexp_AID, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obsexp_UT, y = log_obsexp_AID, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
minValue <- -8
maxValue <- 7
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp_Async.tsv\))) %>%
dplyr::mutate(log_obsexp_UT = if_else(obsexp_UT == 0, minValue, log2(obsexp_UT)),
log_obsexp_AID = if_else(obsexp_AID == 0, minValue, log2(obsexp_AID)))
# dTAG
obsexp$density <- get_density(obsexp$log_obsexp_UT, obsexp$log_obsexp_AID, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_UT, y = log_obsexp_AID, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obsexp_UT, temp$log_obsexp_AID, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obsexp_UT, y = log_obsexp_AID, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)
<!-- rnb-source-begin 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-->
```r
minValue <- -20
maxValue <- 10
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp_Async.tsv\))) %>%
dplyr::mutate(log_obsexp_UT = if_else(obsexp_UT == 0, minValue, log2(obsexp_UT)),
log_obsexp_AID = if_else(obsexp_AID == 0, minValue, log2(obsexp_AID)),
log_obs_UT = if_else(obs_UT == 0, minValue, log2(obs_UT)),
log_obs_AID = if_else(obs_AID == 0, minValue, log2(obs_AID)))
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
# dTAG
obsexp$density <- get_density(obsexp$log_obs_UT, obsexp$log_obs_AID, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obs_UT, y = log_obs_AID, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obs_UT, temp$log_obs_AID, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obs_UT, y = log_obs_AID, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)```r
minValue <- -20
maxValue <- 10
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp_Async.tsv\))) %>%
dplyr::mutate(log_obsexp_UT = if_else(obsexp_UT == 0, minValue, log2(obsexp_UT)),
log_obsexp_AID = if_else(obsexp_AID == 0, minValue, log2(obsexp_AID)),
log_obs_UT = if_else(obs_UT == 0, minValue, log2(obs_UT)),
log_obs_AID = if_else(obs_AID == 0, minValue, log2(obs_AID)))
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
# dTAG
obsexp$density <- get_density(obsexp$log_obs_UT, obsexp$log_obs_AID, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obs_UT, y = log_obs_AID, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obs_UT, temp$log_obs_AID, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obs_UT, y = log_obs_AID, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)
<!-- rnb-source-begin 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 -->
```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
for(res in c(25, 10, 5)){
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp_\, res, \kb.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# A485
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_A485, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(res, \kb```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
for(res in c(25, 10, 5)){
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp_\, res, \kb.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# A485
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_A485, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(res, \kb
<!-- rnb-source-begin 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-->
```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# A485
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_A485, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obsexp_DMSO, temp$log_obsexp_A485, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)```r
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
minValue <- -4
maxValue <- 5
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# A485
obsexp$density <- get_density(obsexp$log_obsexp_DMSO, obsexp$log_obsexp_A485, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obsexp_DMSO, temp$log_obsexp_A485, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs/exp)
<!-- rnb-source-begin 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 -->
```r
minValue <- -4
maxValue <- 10
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obs_DMSO = if_else(obs_DMSO == 0, minValue, log2(obs_DMSO)),
log_obs_dTAG = if_else(obs_dTAG == 0, minValue, log2(obs_dTAG)),
log_obs_A485 = if_else(obs_A485 == 0, minValue, log2(obs_A485)))
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
# dTAG
obsexp$density <- get_density(obsexp$log_obs_DMSO, obsexp$log_obs_A485, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obs_DMSO, y = log_obs_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obs_DMSO, temp$log_obs_A485, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obs_DMSO, y = log_obs_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)```r
minValue <- -4
maxValue <- 10
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obs_DMSO = if_else(obs_DMSO == 0, minValue, log2(obs_DMSO)),
log_obs_dTAG = if_else(obs_dTAG == 0, minValue, log2(obs_dTAG)),
log_obs_A485 = if_else(obs_A485 == 0, minValue, log2(obs_A485)))
# Importing loops
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_A485vsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(binSize = V3 - V2,
id = paste(V1, binSize, V2, V5, sep = \_\))
# dTAG
obsexp$density <- get_density(obsexp$log_obs_DMSO, obsexp$log_obs_A485, n = 100)
obsexp <- obsexp %>% dplyr::arrange(density)
g1 <- ggplot(obsexp, aes(x = log_obs_DMSO, y = log_obs_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)\)) +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue))
# Visualize UP DOWN loops
temp <- obsexp %>% dplyr::filter(id %in% loop.down$id)
temp$density <- get_density(temp$log_obs_DMSO, temp$log_obs_A485, n = 100)
temp <- temp %>% dplyr::arrange(density)
g2 <- ggplot(temp, aes(x = log_obs_DMSO, y = log_obs_A485, color = density)) +
geom_point() + theme_bw() + scale_color_viridis(guide = \none\) +
geom_abline(slope = 1, intercept = 0) +
ggtitle(paste0(\log2(obs)
<!-- rnb-source-begin {"data":"```r\ncreate_dist_vs_avgScore_perTreatment_oe <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_smooth(show.legend = TRUE, se = se)  +\n    #ylim(0, 0.5) +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \"Distance\",\n         y = \"Average Score\") +\n    theme(plot.title = element_text(size = 8))\n  \n  fileName <- paste0(\"dist_vs_score_linePlot_\", name)\n  png(here(figDir, paste0(fileName, \".png\")), \n      res = 600, units = \"in\", width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \".svg\")), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_dist_vs_avgDiffScore_perTreatment_oe <- function(data, figDir, name, loopList, colorList, se = FALSE){\n  avg_scores_long <- data %>%\n    group_by(distance, Anno2) %>%\n    summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n    ungroup() \n  avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)\n  p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) + \n    geom_hline(yintercept = 0) +\n    geom_smooth(show.legend = TRUE, se = se)  +\n    theme_classic() +  scale_x_continuous(labels = label_kb_mb) +\n    scale_color_manual(values = colorList) +\n    scale_fill_manual(values = colorList) +\n    labs(title = paste0(name),\n         x = \"Distance\",\n         y = \"Average Diff Score\") +\n    theme(plot.title = element_text(size = 8)) \n  \n  fileName <- paste0(\"dist_vs_score_difflinePlot_\", name)\n  png(here(figDir, paste0(fileName, \".png\")), \n      res = 600, units = \"in\", width = 4, height = 3)\n  print(p4)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \".svg\")), \n          width = 4, height = 3)\n  print(p4)\n  dev.off()\n} \n\ncreate_dist_barplot_oe <- function(data, figDir, name, note, loopList, diffCutoff, distanceFilter = 2*e6){\n    data <- data %>% \n    dplyr::mutate(distance = start2 - start1,\n                  updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \"UP\", \n                                            ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \"NO\", \"DOWN\")),\n                  updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \"UP\", \n                                            ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \"NO\", \"DOWN\"))) %>%\n    dplyr::filter(Anno2 %in% loopList,\n                  distance < distanceFilter)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\"UP\", \"NO\", \"DOWN\"))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\"UP\", \"NO\", \"DOWN\"))\n  \n  \n  temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%\n    dplyr::filter(Anno2 %in% loopList,\n                  updown_dTAG_DMSO %in% c(\"UP\", \"NO\", \"DOWN\"))\n  p <- ggplot(temp, aes(x = updown_dTAG_DMSO, y = distance)) +\n    geom_violin(aes(fill = updown_dTAG_DMSO)) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = updown_dTAG_DMSO), fun = mean, geom = \"point\", shape = 21, size = 2, fill = \"red\", color = \"black\") +\n    theme_classic() + ggtitle(note) +\n    scale_y_continuous(labels = label_kb_mb)\n  fileName <- paste0(\"size_barplot_\", name, \"_dTAG_vs_DMSO_\", note, \"_\", diffCutoff)\n  height <- 3\n  width <- 4\n  png(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\n  print(p)\n  dev.off()\n  \n  \n  temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%\n    dplyr::filter(Anno2 %in% loopList,\n                  updown_A485_DMSO %in% c(\"UP\", \"NO\",  \"DOWN\"))\n  p <- ggplot(temp, aes(x = updown_A485_DMSO, y = distance)) +\n    geom_violin(aes(fill = updown_A485_DMSO)) + \n    geom_boxplot(width = 0.1, outlier.shape = NA) +\n    stat_summary(aes(group = updown_A485_DMSO), fun = mean, geom = \"point\", shape = 21, size = 2, fill = \"red\", color = \"black\") +\n    theme_classic() + ggtitle(note) +\n    scale_y_continuous(labels = label_kb_mb)\n  fileName <- paste0(\"size_barplot_\", name, \"_A485_vs_DMSO_\", note, \"_\", diffCutoff)\n  height <- 3\n  width <- 4\n  png(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\ncreate_loop_scatterplot_oe <- function(data, figDir, name, Anno2List, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \"UP\", \n                                            ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \"NO\", \"DOWN\")),\n                  updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \"UP\", \n                                            ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \"NO\", \"DOWN\"))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\"UP\", \"NO\", \"DOWN\"))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\"UP\", \"NO\", \"DOWN\"))\n  \n  num.up <- (summary(data$updown_dTAG_DMSO))[\"UP\"]\n  num.no <- (summary(data$updown_dTAG_DMSO))[\"NO\"]\n  num.down <- (summary(data$updown_dTAG_DMSO))[\"DOWN\"]\n  num.all <- num.up + num.no + num.down\n  perc.up <- round(num.up / num.all * 100, 2)\n  perc.no <- round(num.no / num.all * 100, 2)\n  perc.down <- round(num.down / num.all * 100, 2)\n  \n  ### Scatterplot\n    minValue <- -4\n  maxValue <- 5\n  data$density <- get_density(data$log_obsexp_DMSO, data$log_obsexp_dTAG, n = 100)\n  data <- data %>% dplyr::arrange(density)\n  correlation <- cor(data$log_obsexp_DMSO, data$log_obsexp_dTAG)\n  p1 <- ggplot(data, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +\n    geom_point(show.legend = FALSE) + \n    scale_color_viridis() +\n    coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue)) +\n    geom_abline(slope = 1, intercept = 0, col = \"grey50\", linetype = \"dashed\") +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \"grey\", linetype = \"dotted\") +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \"grey\", linetype = \"dotted\") +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \"grey\") +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \"grey\") +\n    annotate(\"text\", x = minValue, y = maxValue, label = paste0(\"UP: \", num.up, \" (\", perc.up, \"%)\"), \n             color = \"black\", hjust = 0, size = 3) +\n    annotate(\"text\", x = minValue, y = maxValue-1, label = paste0(\"NO: \", num.no, \" (\", perc.no, \"%)\"), \n             color = \"black\", hjust = 0, size = 3) +\n    annotate(\"text\", x = minValue, y = maxValue-2, label = paste0(\"DOWN: \", num.down, \" (\", perc.down, \"%)\"), \n             color = \"black\", hjust = 0, size = 3) +\n    theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5)) +\n    annotate(\"text\", x = minValue, y = maxValue, label = paste(\"r =\", round(correlation, 2)), size = 5, color = \"black\")\n\n  \n  \n  num.up <- (summary(data$updown_A485_DMSO))[\"UP\"]\n  num.no <- (summary(data$updown_A485_DMSO))[\"NO\"]\n  num.down <- (summary(data$updown_A485_DMSO))[\"DOWN\"]\n  num.all <- num.up + num.no + num.down\n  perc.up <- round(num.up / num.all * 100, 2)\n  perc.no <- round(num.no / num.all * 100, 2)\n  perc.down <- round(num.down / num.all * 100, 2)\n  \n  data$density <- get_density(data$log_obsexp_DMSO, data$log_obsexp_A485, n = 100)\n  data <- data %>% dplyr::arrange(density)\n  correlation <- cor(data$log_obsexp_DMSO, data$log_obsexp_A485)\n  p2 <- ggplot(data, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +\n    geom_point(show.legend = FALSE) + \n    scale_color_viridis() +\n    coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue)) +\n    geom_abline(slope = 1, intercept = 0, col = \"grey50\", linetype = \"dashed\") +\n    geom_abline(slope = 1, intercept = -diffCutoff, col = \"grey\", linetype = \"dotted\") +\n    geom_abline(slope = 1, intercept = diffCutoff, col = \"grey\", linetype = \"dotted\") +\n    geom_hline(yintercept = 0, alpha = 0.5, color = \"grey\") +\n    geom_vline(xintercept = 0, alpha = 0.5, color = \"grey\") +\n    annotate(\"text\", x = minValue, y = maxValue, label = paste0(\"UP: \", num.up, \" (\", perc.up, \"%)\"), \n             color = \"black\", hjust = 0, size = 3) +\n    annotate(\"text\", x = minValue, y = maxValue-1, label = paste0(\"NO: \", num.no, \" (\", perc.no, \"%)\"), \n             color = \"black\", hjust = 0, size = 3) +\n    annotate(\"text\", x = minValue, y = maxValue-2, label = paste0(\"DOWN: \", num.down, \" (\", perc.down, \"%)\"), \n             color = \"black\", hjust = 0, size = 3) +\n    theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5)) +\n    annotate(\"text\", x = minValue, y = maxValue, label = paste(\"r =\", round(correlation, 2)), size = 5, color = \"black\")\n\n  \n  \n  fileName <- paste0(\"scatterplot_\", name, \"_\", diffCutoff)\n  png(here(figDir, paste0(fileName, \".png\")), res = 600, units = \"in\", width = 5*1.5, height = 2.5*1.5)\n  print(cowplot::plot_grid(p1, p2, align = \"h\"))\n  dev.off()\n  \n  svglite(here(figDir, paste0(fileName, \".svg\")), \n          width = 5*1.5, height = 2.5*1.5)\n  print(cowplot::plot_grid(p1, p2, align = \"h\"))\n  dev.off()\n}\n\nmake_diff_bedpe_oe <- function(data, name, Anno2List, outDir, diffCutoff){\n  data <- data %>%\n    dplyr::mutate(updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \"UP\", \n                                            ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \"NO\", \"DOWN\")),\n                  updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \"UP\", \n                                            ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \"NO\", \"DOWN\"))) %>%\n    dplyr::filter(Anno2 %in% Anno2List)\n  data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\"UP\", \"NO\", \"DOWN\"))\n  data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\"UP\", \"NO\", \"DOWN\"))\n  \n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \"UP\") %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \"_dTAGvsDMSO_UP_diff\", diffCutoff, \".bedpe\")), \n         sep = \"\\t\", col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \"NO\") %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \"_dTAGvsDMSO_NO_diff\", diffCutoff, \".bedpe\")), \n         sep = \"\\t\", col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \"DOWN\") %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \"_dTAGvsDMSO_DOWN_diff\", diffCutoff, \".bedpe\")), \n         sep = \"\\t\", col.names = FALSE)\n  \n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \"UP\") %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \"_A485vsDMSO_UP_diff\", diffCutoff, \".bedpe\")), \n         sep = \"\\t\", col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \"NO\") %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \"_A485vsDMSO_NO_diff\", diffCutoff, \".bedpe\")), \n         sep = \"\\t\", col.names = FALSE)\n  out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \"DOWN\") %>% dplyr::select(c(1, 2, 3, 4, 5, 6))\n  fwrite(out.temp, here(outDir, paste0(name, \"_A485vsDMSO_DOWN_diff\", diffCutoff, \".bedpe\")), \n         sep = \"\\t\", col.names = FALSE)\n}\n```"} -->
```r
create_dist_vs_avgScore_perTreatment_oe <- function(data, figDir, name, loopList, colorList, se = FALSE){
avg_scores_long <- data %>%
group_by(distance, Anno2) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) +
geom_smooth(show.legend = TRUE, se = se) +
#ylim(0, 0.5) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Score\) +
theme(plot.title = element_text(size = 8))
fileName <- paste0(\dist_vs_score_linePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_dist_vs_avgDiffScore_perTreatment_oe <- function(data, figDir, name, loopList, colorList, se = FALSE){
avg_scores_long <- data %>%
group_by(distance, Anno2) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) +
geom_hline(yintercept = 0) +
geom_smooth(show.legend = TRUE, se = se) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Diff Score\) +
theme(plot.title = element_text(size = 8))
fileName <- paste0(\dist_vs_score_difflinePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_dist_barplot_oe <- function(data, figDir, name, note, loopList, diffCutoff, distanceFilter = 2*e6){
data <- data %>%
dplyr::mutate(distance = start2 - start1,
updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% loopList,
distance < distanceFilter)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%
dplyr::filter(Anno2 %in% loopList,
updown_dTAG_DMSO %in% c(\UP\, \NO\, \DOWN\))
p <- ggplot(temp, aes(x = updown_dTAG_DMSO, y = distance)) +
geom_violin(aes(fill = updown_dTAG_DMSO)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = updown_dTAG_DMSO), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + ggtitle(note) +
scale_y_continuous(labels = label_kb_mb)
fileName <- paste0(\size_barplot_\, name, \_dTAG_vs_DMSO_\, note, \_\, diffCutoff)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%
dplyr::filter(Anno2 %in% loopList,
updown_A485_DMSO %in% c(\UP\, \NO\, \DOWN\))
p <- ggplot(temp, aes(x = updown_A485_DMSO, y = distance)) +
geom_violin(aes(fill = updown_A485_DMSO)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = updown_A485_DMSO), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + ggtitle(note) +
scale_y_continuous(labels = label_kb_mb)
fileName <- paste0(\size_barplot_\, name, \_A485_vs_DMSO_\, note, \_\, diffCutoff)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
create_loop_scatterplot_oe <- function(data, figDir, name, Anno2List, diffCutoff){
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% Anno2List)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
### Scatterplot
minValue <- -4
maxValue <- 5
data$density <- get_density(data$log_obsexp_DMSO, data$log_obsexp_dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
correlation <- cor(data$log_obsexp_DMSO, data$log_obsexp_dTAG)
p1 <- ggplot(data, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue)) +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = minValue, y = maxValue, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = minValue, y = maxValue-1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = minValue, y = maxValue-2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5)) +
annotate(\text\, x = minValue, y = maxValue, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
num.up <- (summary(data$updown_A485_DMSO))[\UP\]
num.no <- (summary(data$updown_A485_DMSO))[\NO\]
num.down <- (summary(data$updown_A485_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$log_obsexp_DMSO, data$log_obsexp_A485, n = 100)
data <- data %>% dplyr::arrange(density)
correlation <- cor(data$log_obsexp_DMSO, data$log_obsexp_A485)
p2 <- ggplot(data, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue)) +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = minValue, y = maxValue, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = minValue, y = maxValue-1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = minValue, y = maxValue-2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5)) +
annotate(\text\, x = minValue, y = maxValue, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
}
make_diff_bedpe_oe <- function(data, name, Anno2List, outDir, diffCutoff){
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% Anno2List)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \UP\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_UP_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \NO\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_NO_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \DOWN\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_DOWN_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \UP\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_UP_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \NO\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_NO_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \DOWN\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_DOWN_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
}
```
<!-- rnb-source-end -->```r
create_dist_vs_avgScore_perTreatment_oe <- function(data, figDir, name, loopList, colorList, se = FALSE){
avg_scores_long <- data %>%
group_by(distance, Anno2) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) +
geom_smooth(show.legend = TRUE, se = se) +
#ylim(0, 0.5) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Score\) +
theme(plot.title = element_text(size = 8))
fileName <- paste0(\dist_vs_score_linePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_dist_vs_avgDiffScore_perTreatment_oe <- function(data, figDir, name, loopList, colorList, se = FALSE){
avg_scores_long <- data %>%
group_by(distance, Anno2) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno2 <- factor(avg_scores_long$Anno2, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno2, fill = Anno2)) +
geom_hline(yintercept = 0) +
geom_smooth(show.legend = TRUE, se = se) +
theme_classic() + scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(title = paste0(name),
x = \Distance\,
y = \Average Diff Score\) +
theme(plot.title = element_text(size = 8))
fileName <- paste0(\dist_vs_score_difflinePlot_\, name)
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = 4, height = 3)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 4, height = 3)
print(p4)
dev.off()
}
create_dist_barplot_oe <- function(data, figDir, name, note, loopList, diffCutoff, distanceFilter = 2*e6){
data <- data %>%
dplyr::mutate(distance = start2 - start1,
updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% loopList,
distance < distanceFilter)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%
dplyr::filter(Anno2 %in% loopList,
updown_dTAG_DMSO %in% c(\UP\, \NO\, \DOWN\))
p <- ggplot(temp, aes(x = updown_dTAG_DMSO, y = distance)) +
geom_violin(aes(fill = updown_dTAG_DMSO)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = updown_dTAG_DMSO), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + ggtitle(note) +
scale_y_continuous(labels = label_kb_mb)
fileName <- paste0(\size_barplot_\, name, \_dTAG_vs_DMSO_\, note, \_\, diffCutoff)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
temp <- data %>% dplyr::select(Anno2, distance, updown_dTAG_DMSO, updown_A485_DMSO) %>%
dplyr::filter(Anno2 %in% loopList,
updown_A485_DMSO %in% c(\UP\, \NO\, \DOWN\))
p <- ggplot(temp, aes(x = updown_A485_DMSO, y = distance)) +
geom_violin(aes(fill = updown_A485_DMSO)) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = updown_A485_DMSO), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() + ggtitle(note) +
scale_y_continuous(labels = label_kb_mb)
fileName <- paste0(\size_barplot_\, name, \_A485_vs_DMSO_\, note, \_\, diffCutoff)
height <- 3
width <- 4
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
create_loop_scatterplot_oe <- function(data, figDir, name, Anno2List, diffCutoff){
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% Anno2List)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
num.up <- (summary(data$updown_dTAG_DMSO))[\UP\]
num.no <- (summary(data$updown_dTAG_DMSO))[\NO\]
num.down <- (summary(data$updown_dTAG_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
### Scatterplot
minValue <- -4
maxValue <- 5
data$density <- get_density(data$log_obsexp_DMSO, data$log_obsexp_dTAG, n = 100)
data <- data %>% dplyr::arrange(density)
correlation <- cor(data$log_obsexp_DMSO, data$log_obsexp_dTAG)
p1 <- ggplot(data, aes(x = log_obsexp_DMSO, y = log_obsexp_dTAG, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue)) +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = minValue, y = maxValue, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = minValue, y = maxValue-1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = minValue, y = maxValue-2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5)) +
annotate(\text\, x = minValue, y = maxValue, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
num.up <- (summary(data$updown_A485_DMSO))[\UP\]
num.no <- (summary(data$updown_A485_DMSO))[\NO\]
num.down <- (summary(data$updown_A485_DMSO))[\DOWN\]
num.all <- num.up + num.no + num.down
perc.up <- round(num.up / num.all * 100, 2)
perc.no <- round(num.no / num.all * 100, 2)
perc.down <- round(num.down / num.all * 100, 2)
data$density <- get_density(data$log_obsexp_DMSO, data$log_obsexp_A485, n = 100)
data <- data %>% dplyr::arrange(density)
correlation <- cor(data$log_obsexp_DMSO, data$log_obsexp_A485)
p2 <- ggplot(data, aes(x = log_obsexp_DMSO, y = log_obsexp_A485, color = density)) +
geom_point(show.legend = FALSE) +
scale_color_viridis() +
coord_fixed(xlim = c(minValue, maxValue), ylim = c(minValue, maxValue)) +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_abline(slope = 1, intercept = -diffCutoff, col = \grey\, linetype = \dotted\) +
geom_abline(slope = 1, intercept = diffCutoff, col = \grey\, linetype = \dotted\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
annotate(\text\, x = minValue, y = maxValue, label = paste0(\UP: \, num.up, \ (\, perc.up, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = minValue, y = maxValue-1, label = paste0(\NO: \, num.no, \ (\, perc.no, \%)\),
color = \black\, hjust = 0, size = 3) +
annotate(\text\, x = minValue, y = maxValue-2, label = paste0(\DOWN: \, num.down, \ (\, perc.down, \%)\),
color = \black\, hjust = 0, size = 3) +
theme_classic() + ggtitle(name) + theme(plot.title = element_text(size = 5)) +
annotate(\text\, x = minValue, y = maxValue, label = paste(\r =\, round(correlation, 2)), size = 5, color = \black\)
fileName <- paste0(\scatterplot_\, name, \_\, diffCutoff)
png(here(figDir, paste0(fileName, \.png\)), res = 600, units = \in\, width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = 5*1.5, height = 2.5*1.5)
print(cowplot::plot_grid(p1, p2, align = \h\))
dev.off()
}
make_diff_bedpe_oe <- function(data, name, Anno2List, outDir, diffCutoff){
data <- data %>%
dplyr::mutate(updown_dTAG_DMSO = ifelse(log_obsexp_diff_dTAG_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_dTAG_DMSO > -diffCutoff, \NO\, \DOWN\)),
updown_A485_DMSO = ifelse(log_obsexp_diff_A485_DMSO > diffCutoff, \UP\,
ifelse(log_obsexp_diff_A485_DMSO > -diffCutoff, \NO\, \DOWN\))) %>%
dplyr::filter(Anno2 %in% Anno2List)
data$updown_dTAG_DMSO <- factor(data$updown_dTAG_DMSO, levels = c(\UP\, \NO\, \DOWN\))
data$updown_A485_DMSO <- factor(data$updown_A485_DMSO, levels = c(\UP\, \NO\, \DOWN\))
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \UP\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_UP_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \NO\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_NO_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_dTAG_DMSO == \DOWN\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_dTAGvsDMSO_DOWN_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \UP\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_UP_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \NO\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_NO_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
out.temp <- data %>% dplyr::filter(updown_A485_DMSO == \DOWN\) %>% dplyr::select(c(1, 2, 3, 4, 5, 6))
fwrite(out.temp, here(outDir, paste0(name, \_A485vsDMSO_DOWN_diff\, diffCutoff, \.bedpe\)),
sep = \\t\, col.names = FALSE)
}
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###### Plotting Obs
<!-- rnb-text-end -->
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<!-- rnb-output-begin 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# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\))
# Plot
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obsexp_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_DMSO_logOE\), unique(data$Anno2), colorListLoop)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obsexp_dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_dTAG_logOE\), unique(data$Anno2), colorListLoop)
# Creating figures per each condition, differential
temp <- data %>% dplyr::mutate(distance = start2 - start1,
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO) %>%
dplyr::select(distance, log_obsexp_diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment_oe(temp, figDir, paste0(name, \_dTAG_logOE\), unique(data$Anno2), colorListLoop)
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```r
```r
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\))
# Plot
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obsexp_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_DMSO_logOE\), unique(data$Anno2), colorListLoop)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obsexp_dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_dTAG_logOE\), unique(data$Anno2), colorListLoop)
# Creating figures per each condition, differential
temp <- data %>% dplyr::mutate(distance = start2 - start1,
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO) %>%
dplyr::select(distance, log_obsexp_diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment_oe(temp, figDir, paste0(name, \_dTAG_logOE\), unique(data$Anno2), colorListLoop)
```
```
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### [2.26] Repeating few analysis with obs/exp
#### Function
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-->\n\n```r\n# Import annotation\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n\n# Import obs/exp scores and merge to the dataset\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n    dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                  log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                  log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))\n\n# Merge dataset\ndata <- data %>% dplyr::left_join(obsexp, by = c(\\id\\))\n\ntemp <- data %>% dplyr::mutate(distance = start2 - start1,\n                               log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO) %>% \n  dplyr::select(distance, log_obsexp_diff_dTAG_DMSO, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\n\n\n\ntemp <- temp %>% dplyr::mutate(Anno3 = case_when(Anno2 %in% c(\\S-X\\, \\S-S\\) ~ \\Structural\\,\n                                                 Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\) ~ \\Pure_Regulatory\\,\n                                                 Anno2 %in% c(\\P-S\\, \\P-X\\, \\E-S\\, \\E-X\\) ~ \\Relaxed_Regulatory\\)) %>%\n  dplyr::filter(!is.na(Anno3))\n\nloopList <- rev(c(\\Structural\\, \\Relaxed_Regulatory\\, \\Pure_Regulatory\\))\ncolorList <- rev(c(palette_3[[\\grey2\\]], strong_green, darken(strong_green, amount = 0.5)))\navg_scores_long <- temp %>%\n  group_by(distance, Anno3) %>%\n  summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n  ungroup() \navg_scores_long$Anno3 <- factor(avg_scores_long$Anno3, level = loopList)\n\np4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno3)) + \n  geom_hline(yintercept = 0,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_smooth(show.legend = TRUE, se = TRUE,\n              size = lineThick*mmToLineUnit,\n              lineend = \\square\\,\n              aes(fill = Anno3))  +\n  theme_classic() + \n  scale_x_continuous(labels = label_kb_mb) +\n  scale_color_manual(values = colorList) +\n  scale_fill_manual(values = colorList) +\n  labs(x = \\Loop size\\,\n       y = \\Δ log2(obs/exp)\\,\n       color = \\Loop types\\, fill = \\Loop types\\) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +\n  guides(\n    color = guide_legend(\n      keywidth = 0.5,  # Adjust the width of the legend color squares\n      keyheight = 0.5  # Adjust the height of the legend color squares\n    )\n  )\n\n\n\n\n\nfileName <- paste0(\\dist_vs_score_difflinePlot_grouped\\, name)\n\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.25)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), \n    res = 600, units = \\in\\, width = width, height = height)\nprint(p4)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)), \n        width = width, height = height)\nprint(p4)\ndev.off()\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\))
temp <- data %>% dplyr::mutate(distance = start2 - start1,
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO) %>%
dplyr::select(distance, log_obsexp_diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
temp <- temp %>% dplyr::mutate(Anno3 = case_when(Anno2 %in% c(\S-X\, \S-S\) ~ \Structural\,
Anno2 %in% c(\P-P\, \P-E\, \E-E\) ~ \Pure_Regulatory\,
Anno2 %in% c(\P-S\, \P-X\, \E-S\, \E-X\) ~ \Relaxed_Regulatory\)) %>%
dplyr::filter(!is.na(Anno3))
loopList <- rev(c(\Structural\, \Relaxed_Regulatory\, \Pure_Regulatory\))
colorList <- rev(c(palette_3[[\grey2\]], strong_green, darken(strong_green, amount = 0.5)))
avg_scores_long <- temp %>%
group_by(distance, Anno3) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno3 <- factor(avg_scores_long$Anno3, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno3)) +
geom_hline(yintercept = 0,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_smooth(show.legend = TRUE, se = TRUE,
size = lineThick*mmToLineUnit,
lineend = \square\,
aes(fill = Anno3)) +
theme_classic() +
scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(x = \Loop size\,
y = \Δ log2(obs/exp)\,
color = \Loop types\, fill = \Loop types\) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
guides(
color = guide_legend(
keywidth = 0.5, # Adjust the width of the legend color squares
keyheight = 0.5 # Adjust the height of the legend color squares
)
)
fileName <- paste0(\dist_vs_score_difflinePlot_grouped\, name)
width <- panelSize(2.5)*mmToInch
height <- panelSize(1.25)*mmToInch
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = width, height = height)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = width, height = height)
print(p4)
dev.off()
<!-- rnb-output-end -->
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-->\n\n```r\n```r\n# Import annotation\nname <- \\chromo_cons_annoHierarchy\\\ndata <- fread(here(consensusDir, paste0(name, \\.tsv\\)))\n\n# Import obs/exp scores and merge to the dataset\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n    dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                  log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                  log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))\n\n# Merge dataset\ndata <- data %>% dplyr::left_join(obsexp, by = c(\\id\\))\n\ntemp <- data %>% dplyr::mutate(distance = start2 - start1,\n                               log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO) %>% \n  dplyr::select(distance, log_obsexp_diff_dTAG_DMSO, Anno2)\ncolnames(temp) <- c(\\distance\\, \\score\\, \\Anno2\\)\n\n\n\ntemp <- temp %>% dplyr::mutate(Anno3 = case_when(Anno2 %in% c(\\S-X\\, \\S-S\\) ~ \\Structural\\,\n                                                 Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\) ~ \\Pure_Regulatory\\,\n                                                 Anno2 %in% c(\\P-S\\, \\P-X\\, \\E-S\\, \\E-X\\) ~ \\Relaxed_Regulatory\\)) %>%\n  dplyr::filter(!is.na(Anno3))\n\nloopList <- rev(c(\\Structural\\, \\Relaxed_Regulatory\\, \\Pure_Regulatory\\))\ncolorList <- rev(c(palette_3[[\\grey2\\]], strong_green, darken(strong_green, amount = 0.5)))\navg_scores_long <- temp %>%\n  group_by(distance, Anno3) %>%\n  summarise(avg_score = mean(score, na.rm = TRUE)) %>%\n  ungroup() \navg_scores_long$Anno3 <- factor(avg_scores_long$Anno3, level = loopList)\n\np4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno3)) + \n  geom_hline(yintercept = 0,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) +\n  geom_smooth(show.legend = TRUE, se = TRUE,\n              size = lineThick*mmToLineUnit,\n              lineend = \\square\\,\n              aes(fill = Anno3))  +\n  theme_classic() + \n  scale_x_continuous(labels = label_kb_mb) +\n  scale_color_manual(values = colorList) +\n  scale_fill_manual(values = colorList) +\n  labs(x = \\Loop size\\,\n       y = \\Δ log2(obs/exp)\\,\n       color = \\Loop types\\, fill = \\Loop types\\) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +\n  guides(\n    color = guide_legend(\n      keywidth = 0.5,  # Adjust the width of the legend color squares\n      keyheight = 0.5  # Adjust the height of the legend color squares\n    )\n  )\n\n\n\n\n\nfileName <- paste0(\\dist_vs_score_difflinePlot_grouped\\, name)\n\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.25)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), \n    res = 600, units = \\in\\, width = width, height = height)\nprint(p4)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)), \n        width = width, height = height)\nprint(p4)\ndev.off()\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\))
temp <- data %>% dplyr::mutate(distance = start2 - start1,
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO) %>%
dplyr::select(distance, log_obsexp_diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
temp <- temp %>% dplyr::mutate(Anno3 = case_when(Anno2 %in% c(\S-X\, \S-S\) ~ \Structural\,
Anno2 %in% c(\P-P\, \P-E\, \E-E\) ~ \Pure_Regulatory\,
Anno2 %in% c(\P-S\, \P-X\, \E-S\, \E-X\) ~ \Relaxed_Regulatory\)) %>%
dplyr::filter(!is.na(Anno3))
loopList <- rev(c(\Structural\, \Relaxed_Regulatory\, \Pure_Regulatory\))
colorList <- rev(c(palette_3[[\grey2\]], strong_green, darken(strong_green, amount = 0.5)))
avg_scores_long <- temp %>%
group_by(distance, Anno3) %>%
summarise(avg_score = mean(score, na.rm = TRUE)) %>%
ungroup()
avg_scores_long$Anno3 <- factor(avg_scores_long$Anno3, level = loopList)
p4 <- ggplot(avg_scores_long, aes(x = distance, y = avg_score, color = Anno3)) +
geom_hline(yintercept = 0,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_smooth(show.legend = TRUE, se = TRUE,
size = lineThick*mmToLineUnit,
lineend = \square\,
aes(fill = Anno3)) +
theme_classic() +
scale_x_continuous(labels = label_kb_mb) +
scale_color_manual(values = colorList) +
scale_fill_manual(values = colorList) +
labs(x = \Loop size\,
y = \Δ log2(obs/exp)\,
color = \Loop types\, fill = \Loop types\) +
theme(
axis.title = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
guides(
color = guide_legend(
keywidth = 0.5, # Adjust the width of the legend color squares
keyheight = 0.5 # Adjust the height of the legend color squares
)
)
fileName <- paste0(\dist_vs_score_difflinePlot_grouped\, name)
width <- panelSize(2.5)*mmToInch
height <- panelSize(1.25)*mmToInch
png(here(figDir, paste0(fileName, \.png\)),
res = 600, units = \in\, width = width, height = height)
print(p4)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)),
width = width, height = height)
print(p4)
dev.off()
```
```
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#### Distance vs score plot
##### dTAG
###### OE
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<!-- rnb-output-begin 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-->
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obs_DMSO = if_else(obs_DMSO == 0, minValue, log2(obs_DMSO)),
log_obs_dTAG = if_else(obs_dTAG == 0, minValue, log2(obs_dTAG)),
log_obs_A485 = if_else(obs_A485 == 0, minValue, log2(obs_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\))
# Plot
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obs_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_DMSO_logObs\), unique(data$Anno2), colorListLoop)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obs_dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_dTAG_logObs\), unique(data$Anno2), colorListLoop)
# Creating figures per each condition, differential
temp <- data %>% dplyr::mutate(distance = start2 - start1,
log_obs_diff_dTAG_DMSO = log_obs_dTAG - log_obs_DMSO) %>%
dplyr::select(distance, log_obs_diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment_oe(temp, figDir, paste0(name, \_dTAG_logObs\), unique(data$Anno2), colorListLoop)
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<!-- rnb-output-begin 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-->
<!-- rnb-source-begin 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 -->
```r
```r
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obs_DMSO = if_else(obs_DMSO == 0, minValue, log2(obs_DMSO)),
log_obs_dTAG = if_else(obs_dTAG == 0, minValue, log2(obs_dTAG)),
log_obs_A485 = if_else(obs_A485 == 0, minValue, log2(obs_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\))
# Plot
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obs_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_DMSO_logObs\), unique(data$Anno2), colorListLoop)
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obs_dTAG, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_dTAG_logObs\), unique(data$Anno2), colorListLoop)
# Creating figures per each condition, differential
temp <- data %>% dplyr::mutate(distance = start2 - start1,
log_obs_diff_dTAG_DMSO = log_obs_dTAG - log_obs_DMSO) %>%
dplyr::select(distance, log_obs_diff_dTAG_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment_oe(temp, figDir, paste0(name, \_dTAG_logObs\), unique(data$Anno2), colorListLoop)
```
```
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###### OE, grouped
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# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\))
# Plot
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obsexp_A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_A485_logOE\), unique(data$Anno2), colorListLoop)
# Creating figures per each condition, differential
temp <- data %>% dplyr::mutate(distance = start2 - start1,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO) %>%
dplyr::select(distance, log_obsexp_diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment_oe(temp, figDir, paste0(name, \_A485_logOE\), unique(data$Anno2), colorListLoop)
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```r
```r
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\))
# Plot
temp <- data %>% dplyr::mutate(distance = start2 - start1) %>% dplyr::select(distance, log_obsexp_A485, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgScore_perTreatment_oe(temp, figDir, paste0(name, \_A485_logOE\), unique(data$Anno2), colorListLoop)
# Creating figures per each condition, differential
temp <- data %>% dplyr::mutate(distance = start2 - start1,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO) %>%
dplyr::select(distance, log_obsexp_diff_A485_DMSO, Anno2)
colnames(temp) <- c(\distance\, \score\, \Anno2\)
create_dist_vs_avgDiffScore_perTreatment_oe(temp, figDir, paste0(name, \_A485_logOE\), unique(data$Anno2), colorListLoop)
```
```
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###### Observed
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# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue = -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>%
dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
diffCutoff <- 0.5
create_loop_scatterplot_oe(data, figDir, paste0(name, \_logOE_\, \all\), unique(data$Anno2), diffCutoff)
create_loop_scatterplot_oe(data, figDir, paste0(name, \_logOE_\, \reg\), c(\P-P\, \P-E\, \E-E\), diffCutoff)
create_loop_scatterplot_oe(data, figDir, paste0(name, \_logOE_\, \str\), c(\S-S\, \S-X\), diffCutoff)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \all\, unique(data$Anno2), diffCutoff)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \reg\, c(\P-P\, \P-E\, \E-E\), diffCutoff)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \reg_1mb\, c(\P-P\, \P-E\, \E-E\), diffCutoff, 1e6)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \str\, c(\S-S\, \S-X\), diffCutoff)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \str_1mb\, c(\S-S\, \S-X\), diffCutoff, 1e6)
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-->
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```r
```r
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue = -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>%
dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
diffCutoff <- 0.5
create_loop_scatterplot_oe(data, figDir, paste0(name, \_logOE_\, \all\), unique(data$Anno2), diffCutoff)
create_loop_scatterplot_oe(data, figDir, paste0(name, \_logOE_\, \reg\), c(\P-P\, \P-E\, \E-E\), diffCutoff)
create_loop_scatterplot_oe(data, figDir, paste0(name, \_logOE_\, \str\), c(\S-S\, \S-X\), diffCutoff)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \all\, unique(data$Anno2), diffCutoff)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \reg\, c(\P-P\, \P-E\, \E-E\), diffCutoff)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \reg_1mb\, c(\P-P\, \P-E\, \E-E\), diffCutoff, 1e6)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \str\, c(\S-S\, \S-X\), diffCutoff)
create_dist_barplot_oe(data, figDir, paste0(name, \_logOE\), \str_1mb\, c(\S-S\, \S-X\), diffCutoff, 1e6)
```
```
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##### A485
<!-- rnb-text-end -->
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<!-- rnb-output-begin 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 -->
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue = -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>%
dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
diffCutoff <- 0.5
make_diff_bedpe_oe(data, paste0(name, \_logOE_\, \all\), unique(data$Anno2), consensusDir, diffCutoff)
make_diff_bedpe_oe(data, paste0(name, \_logOE_\, \pe-pe\), c(\P-P\, \P-E\, \E-E\), consensusDir, diffCutoff)
make_diff_bedpe_oe(data, paste0(name, \_logOE_\, \str\), c(\S-S\, \S-X\), consensusDir, diffCutoff)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
# Import annotation
name <- \chromo_cons_annoHierarchy\
data <- fread(here(consensusDir, paste0(name, \.tsv\)))
# Import obs/exp scores and merge to the dataset
minValue = -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)))
# Merge dataset
data <- data %>% dplyr::left_join(obsexp, by = c(\id\)) %>%
dplyr::mutate(log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
diffCutoff <- 0.5
make_diff_bedpe_oe(data, paste0(name, \_logOE_\, \all\), unique(data$Anno2), consensusDir, diffCutoff)
make_diff_bedpe_oe(data, paste0(name, \_logOE_\, \pe-pe\), c(\P-P\, \P-E\, \E-E\), consensusDir, diffCutoff)
make_diff_bedpe_oe(data, paste0(name, \_logOE_\, \str\), c(\S-S\, \S-X\), consensusDir, diffCutoff)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Get size distribution of differential loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
flankSize <- 1
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\)
bed.1 <- gene.tb %>% dplyr::filter(ensembl %in% group1) %>% dplyr::select(c(1, 2, 3))
bed.2 <- gene.tb %>% dplyr::filter(ensembl %in% group2) %>% dplyr::select(c(1, 2, 3))
fwrite(bed.1, here(refDir, \TSS_binaryGroup1.bed\), sep = \\t\, col.names = FALSE)
fwrite(bed.2, here(refDir, \TSS_binaryGroup2.bed\), sep = \\t\, col.names = FALSE)
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\))
bed.1 <- gene.tb %>% dplyr::filter(V6 %in% group1)
bed.2 <- gene.tb %>% dplyr::filter(V6 %in% group2)
fwrite(bed.1, here(refDir, \TSS_binaryGroup1_gtf.bed\), sep = \\t\, col.names = FALSE)
fwrite(bed.2, here(refDir, \TSS_binaryGroup2_gtf.bed\), sep = \\t\, col.names = FALSE)
<!-- rnb-output-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
flankSize <- 1
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\)
bed.1 <- gene.tb %>% dplyr::filter(ensembl %in% group1) %>% dplyr::select(c(1, 2, 3))
bed.2 <- gene.tb %>% dplyr::filter(ensembl %in% group2) %>% dplyr::select(c(1, 2, 3))
fwrite(bed.1, here(refDir, \TSS_binaryGroup1.bed\), sep = \\t\, col.names = FALSE)
fwrite(bed.2, here(refDir, \TSS_binaryGroup2.bed\), sep = \\t\, col.names = FALSE)
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\))
bed.1 <- gene.tb %>% dplyr::filter(V6 %in% group1)
bed.2 <- gene.tb %>% dplyr::filter(V6 %in% group2)
fwrite(bed.1, here(refDir, \TSS_binaryGroup1_gtf.bed\), sep = \\t\, col.names = FALSE)
fwrite(bed.2, here(refDir, \TSS_binaryGroup2_gtf.bed\), sep = \\t\, col.names = FALSE)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Splitting loops into diff bedpe
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- fread(here(refDir, \readCount.filtered.TPM.all.tsv\)) %>% dplyr::mutate(
group = case_when(ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA)
) %>%
dplyr::filter(!is.na(group)) %>%
dplyr::select(c(34, 1, 3, 4, 5, 14, 15, 34))
data <- data %>% mutate(average = rowMeans(across(colnames(data)[3:7])))
temp <- data %>% dplyr::select(group, average)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$average
distance2 <- (data %>% dplyr::filter(group ==group2) )$average
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pval <- convPvalue(getPvalWilcox(temp, \group1\, \group2\))
ggplot(temp, aes(x = group, y = average)) + geom_boxplot() + scale_y_log10() +
ylab(\avgTPM\) + theme_classic() +
annotate(\text\, x = 1, y = 10000, label = pval)
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVozSnZkWEF4SUR3dElHWnlaV0ZrS0dobGNtVW9jbVZtUkdseUxDQmNYR2RsYm1WTWFYTjBYMlJVUVVkZmRuTmZSRTFUVDE5U1RrRmZiRzl2Y0Y5aWFXNWhjbmxIY205MWNERXVkSE4yWEZ3cEtTUm5aVzVsWEc1bmNtOTFjRElnUEMwZ1puSmxZV1FvYUdWeVpTaHlaV1pFYVhJc0lGeGNaMlZ1WlV4cGMzUmZaRlJCUjE5MmMxOUVUVk5QWDFKT1FWOXNiMjl3WDJKcGJtRnllVWR5YjNWd01pNTBjM1pjWENrcEpHZGxibVZjYmx4dVpHRjBZU0E4TFNCbWNtVmhaQ2hvWlhKbEtISmxaa1JwY2l3Z1hGeHlaV0ZrUTI5MWJuUXVabWxzZEdWeVpXUXVWRkJOTG1Gc2JDNTBjM1pjWENrcElDVStKU0JrY0d4NWNqbzZiWFYwWVhSbEtGeHVJQ0JuY205MWNDQTlJR05oYzJWZmQyaGxiaWhsYm5ObGJXSnNJQ1ZwYmlVZ1ozSnZkWEF4SUg0Z1hGeG5jbTkxY0RGY1hDeGNiaUFnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnWlc1elpXMWliQ0FsYVc0bElHZHliM1Z3TWlCK0lGeGNaM0p2ZFhBeVhGd3NYRzRnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUZSU1ZVVWdmaUJPUVNsY2Jpa2dKVDRsWEc0Z0lHUndiSGx5T2pwbWFXeDBaWElvSVdsekxtNWhLR2R5YjNWd0tTa2dKVDRsWEc0Z0lHUndiSGx5T2pwelpXeGxZM1FvWXlnek5Dd2dNU3dnTXl3Z05Dd2dOU3dnTVRRc0lERTFMQ0F6TkNrcFhHNWNibVJoZEdFZ1BDMGdaR0YwWVNBbFBpVWdiWFYwWVhSbEtHRjJaWEpoWjJVZ1BTQnliM2ROWldGdWN5aGhZM0p2YzNNb1kyOXNibUZ0WlhNb1pHRjBZU2xiTXpvM1hTa3BLVnh1WEc1MFpXMXdJRHd0SUdSaGRHRWdKVDRsSUdSd2JIbHlPanB6Wld4bFkzUW9aM0p2ZFhBc0lHRjJaWEpoWjJVcFhHNWNibHh1WjJWMFVIWmhiRmRwYkdOdmVDQThMU0JtZFc1amRHbHZiaWhrWVhSaExDQm5jbTkxY0RFc0lHZHliM1Z3TWlsN1hHNGdJR1JwYzNSaGJtTmxNU0E4TFNBb1pHRjBZU0FsUGlVZ1pIQnNlWEk2T21acGJIUmxjaWhuY205MWNDQTlQV2R5YjNWd01Ta2dLU1JoZG1WeVlXZGxYRzRnSUdScGMzUmhibU5sTWlBOExTQW9aR0YwWVNBbFBpVWdaSEJzZVhJNk9tWnBiSFJsY2lobmNtOTFjQ0E5UFdkeWIzVndNaWtnS1NSaGRtVnlZV2RsWEc0Z0lIZHBiQ0E4TFNCM2FXeGpiM2d1ZEdWemRDaGthWE4wWVc1alpURXNJR1JwYzNSaGJtTmxNaWxjYmlBZ2NtVjBkWEp1S0hkcGJDUndMblpoYkhWbEtWeHVmVnh1WEc1d2RtRnNJRHd0SUdOdmJuWlFkbUZzZFdVb1oyVjBVSFpoYkZkcGJHTnZlQ2gwWlcxd0xDQmNYR2R5YjNWd01WeGNMQ0JjWEdkeWIzVndNbHhjS1NsY2JseHVaMmR3Ykc5MEtIUmxiWEFzSUdGbGN5aDRJRDBnWjNKdmRYQXNJSGtnUFNCaGRtVnlZV2RsS1NrZ0t5Qm5aVzl0WDJKdmVIQnNiM1FvS1NBcklITmpZV3hsWDNsZmJHOW5NVEFvS1NBclhHNGdJSGxzWVdJb1hGeGhkbWRVVUUxY1hDa2dLeUIwYUdWdFpWOWpiR0Z6YzJsaktDa2dLMXh1SUNCaGJtNXZkR0YwWlNoY1hIUmxlSFJjWEN3Z2VDQTlJREVzSUhrZ1BTQXhNREF3TUN3Z2JHRmlaV3dnUFNCd2RtRnNLVnh1WEc1Y2JseHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5ncm91cDEgPC0gZnJlYWQoaGVyZShyZWZEaXIsIFxcZ2VuZUxpc3RfZFRBR192c19ETVNPX1JOQV9sb29wX2JpbmFyeUdyb3VwMS50c3ZcXCkpJGdlbmVcbmdyb3VwMiA8LSBmcmVhZChoZXJlKHJlZkRpciwgXFxnZW5lTGlzdF9kVEFHX3ZzX0RNU09fUk5BX2xvb3BfYmluYXJ5R3JvdXAyLnRzdlxcKSkkZ2VuZVxuXG5kYXRhIDwtIGZyZWFkKGhlcmUocmVmRGlyLCBcXHJlYWRDb3VudC5maWx0ZXJlZC5UUE0uYWxsLnRzdlxcKSkgJT4lIGRwbHlyOjptdXRhdGUoXG4gIGdyb3VwID0gY2FzZV93aGVuKGVuc2VtYmwgJWluJSBncm91cDEgfiBcXGdyb3VwMVxcLFxuICAgICAgICAgICAgICAgICAgICBlbnNlbWJsICVpbiUgZ3JvdXAyIH4gXFxncm91cDJcXCxcbiAgICAgICAgICAgICAgICAgICAgVFJVRSB+IE5BKVxuKSAlPiVcbiAgZHBseXI6OmZpbHRlcighaXMubmEoZ3JvdXApKSAlPiVcbiAgZHBseXI6OnNlbGVjdChjKDM0LCAxLCAzLCA0LCA1LCAxNCwgMTUsIDM0KSlcblxuZGF0YSA8LSBkYXRhICU+JSBtdXRhdGUoYXZlcmFnZSA9IHJvd01lYW5zKGFjcm9zcyhjb2xuYW1lcyhkYXRhKVszOjddKSkpXG5cbnRlbXAgPC0gZGF0YSAlPiUgZHBseXI6OnNlbGVjdChncm91cCwgYXZlcmFnZSlcblxuXG5nZXRQdmFsV2lsY294IDwtIGZ1bmN0aW9uKGRhdGEsIGdyb3VwMSwgZ3JvdXAyKXtcbiAgZGlzdGFuY2UxIDwtIChkYXRhICU+JSBkcGx5cjo6ZmlsdGVyKGdyb3VwID09Z3JvdXAxKSApJGF2ZXJhZ2VcbiAgZGlzdGFuY2UyIDwtIChkYXRhICU+JSBkcGx5cjo6ZmlsdGVyKGdyb3VwID09Z3JvdXAyKSApJGF2ZXJhZ2VcbiAgd2lsIDwtIHdpbGNveC50ZXN0KGRpc3RhbmNlMSwgZGlzdGFuY2UyKVxuICByZXR1cm4od2lsJHAudmFsdWUpXG59XG5cbnB2YWwgPC0gY29udlB2YWx1ZShnZXRQdmFsV2lsY294KHRlbXAsIFxcZ3JvdXAxXFwsIFxcZ3JvdXAyXFwpKVxuXG5nZ3Bsb3QodGVtcCwgYWVzKHggPSBncm91cCwgeSA9IGF2ZXJhZ2UpKSArIGdlb21fYm94cGxvdCgpICsgc2NhbGVfeV9sb2cxMCgpICtcbiAgeWxhYihcXGF2Z1RQTVxcKSArIHRoZW1lX2NsYXNzaWMoKSArXG4gIGFubm90YXRlKFxcdGV4dFxcLCB4ID0gMSwgeSA9IDEwMDAwLCBsYWJlbCA9IHB2YWwpXG5cblxuXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
<!-- rnb-source-begin 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 -->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- fread(here(refDir, \readCount.filtered.TPM.all.tsv\)) %>% dplyr::mutate(
group = case_when(ensembl %in% group1 ~ \group1\,
ensembl %in% group2 ~ \group2\,
TRUE ~ NA)
) %>%
dplyr::filter(!is.na(group)) %>%
dplyr::select(c(34, 1, 3, 4, 5, 14, 15, 34))
data <- data %>% mutate(average = rowMeans(across(colnames(data)[3:7])))
temp <- data %>% dplyr::select(group, average)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$average
distance2 <- (data %>% dplyr::filter(group ==group2) )$average
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pval <- convPvalue(getPvalWilcox(temp, \group1\, \group2\))
ggplot(temp, aes(x = group, y = average)) + geom_boxplot() + scale_y_log10() +
ylab(\avgTPM\) + theme_classic() +
annotate(\text\, x = 1, y = 10000, label = pval)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.27] Extracting TSS for deeptools
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nflankSize <- 1\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\, \\ensembl\\)\n\nbed.1 <- gene.tb %>% dplyr::filter(ensembl %in% group1) %>% dplyr::select(c(1, 2, 3))\nbed.2 <- gene.tb %>% dplyr::filter(ensembl %in% group2) %>% dplyr::select(c(1, 2, 3))\n\n\ndata <- fread(here(\\../..\\, \\result\\, \\stripenn\\, \\result_filtered.tsv\\))\n\nggplot(data, aes(x = width)) + geom_histogram() + theme_classic() +\n scale_x_continuous(labels = label_kb_mb)\n\n\n## Checking overlap with gene TSS\n\ntss <- fread(here(refDir, \\mm10_GRCm38.p6_TSS2.5kb.bed\\))\n\ntss.group1 <- tss %>% dplyr::filter(V6 %in% group1) %>% dplyr::select(V1, V2, V3)\ncolnames(tss.group1) <- c(\\chr\\, \\start\\, \\end\\)\ntss.group1.gr <- makeGRangesFromDataFrame(tss.group1)\n\ntss.group2 <- tss %>% dplyr::filter(V6 %in% group2) %>% dplyr::select(V1, V2, V3)\ncolnames(tss.group2) <- c(\\chr\\, \\start\\, \\end\\)\ntss.group2.gr <- makeGRangesFromDataFrame(tss.group2)\n\n## Anchor of stripes\ntemp <- data %>% dplyr::select(chr, pos1, pos2)\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n\nstripeAnchor.gr <- makeGRangesFromDataFrame(temp)\n\noverlap.group1 <- findOverlaps(tss.group1.gr, stripeAnchor.gr)\noverlap.group2 <- findOverlaps(tss.group2.gr, stripeAnchor.gr)\n\nlength(unique(queryHits(overlap.group1)))\nlength(unique(queryHits(overlap.group2)))\n\n\n## body of stripes\ntemp <- data %>% dplyr::select(chr, pos3, pos4)\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n\nstripeBody.gr <- makeGRangesFromDataFrame(temp)\n\noverlap.group1 <- findOverlaps(tss.group1.gr, stripeBody.gr)\noverlap.group2 <- findOverlaps(tss.group2.gr, stripeBody.gr)\n\nlength(unique(queryHits(overlap.group1)))\nlength(unique(queryHits(overlap.group2)))\n\n## Checking RAD21/CTCF presence at anchor\noverlap.body.ctcf <- findOverlaps(stripeBody.gr, peak.CTCF)\noverlap.body.rad21 <- findOverlaps(stripeBody.gr, peak.RAD21)\noverlap.anchor.ctcf <- findOverlaps(stripeAnchor.gr, peak.CTCF)\noverlap.anchor.rad21 <- findOverlaps(stripeAnchor.gr, peak.RAD21)\n\nlength(unique(queryHits(overlap.body.ctcf)))\nlength(unique(queryHits(overlap.body.rad21)))\n\nlength(unique(queryHits(overlap.anchor.ctcf)))\nlength(unique(queryHits(overlap.anchor.rad21)))\n  \n## Checking overlap with loop anchor (PE-PE)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\n## Checking overlap with loop anchor (ALL)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\n## Checking overlap with loop anchor (Structure)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\nlength(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100\nlength(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100\n\n\n## Checking overlap with loop anchor (RelaxedReg)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\nlength(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100\nlength(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100\n\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
flankSize <- 1
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\)
bed.1 <- gene.tb %>% dplyr::filter(ensembl %in% group1) %>% dplyr::select(c(1, 2, 3))
bed.2 <- gene.tb %>% dplyr::filter(ensembl %in% group2) %>% dplyr::select(c(1, 2, 3))
data <- fread(here(\../..\, \result\, \stripenn\, \result_filtered.tsv\))
ggplot(data, aes(x = width)) + geom_histogram() + theme_classic() +
scale_x_continuous(labels = label_kb_mb)
## Checking overlap with gene TSS
tss <- fread(here(refDir, \mm10_GRCm38.p6_TSS2.5kb.bed\))
tss.group1 <- tss %>% dplyr::filter(V6 %in% group1) %>% dplyr::select(V1, V2, V3)
colnames(tss.group1) <- c(\chr\, \start\, \end\)
tss.group1.gr <- makeGRangesFromDataFrame(tss.group1)
tss.group2 <- tss %>% dplyr::filter(V6 %in% group2) %>% dplyr::select(V1, V2, V3)
colnames(tss.group2) <- c(\chr\, \start\, \end\)
tss.group2.gr <- makeGRangesFromDataFrame(tss.group2)
## Anchor of stripes
temp <- data %>% dplyr::select(chr, pos1, pos2)
colnames(temp) <- c(\chr\, \start\, \end\)
stripeAnchor.gr <- makeGRangesFromDataFrame(temp)
overlap.group1 <- findOverlaps(tss.group1.gr, stripeAnchor.gr)
overlap.group2 <- findOverlaps(tss.group2.gr, stripeAnchor.gr)
length(unique(queryHits(overlap.group1)))
length(unique(queryHits(overlap.group2)))
## body of stripes
temp <- data %>% dplyr::select(chr, pos3, pos4)
colnames(temp) <- c(\chr\, \start\, \end\)
stripeBody.gr <- makeGRangesFromDataFrame(temp)
overlap.group1 <- findOverlaps(tss.group1.gr, stripeBody.gr)
overlap.group2 <- findOverlaps(tss.group2.gr, stripeBody.gr)
length(unique(queryHits(overlap.group1)))
length(unique(queryHits(overlap.group2)))
## Checking RAD21/CTCF presence at anchor
overlap.body.ctcf <- findOverlaps(stripeBody.gr, peak.CTCF)
overlap.body.rad21 <- findOverlaps(stripeBody.gr, peak.RAD21)
overlap.anchor.ctcf <- findOverlaps(stripeAnchor.gr, peak.CTCF)
overlap.anchor.rad21 <- findOverlaps(stripeAnchor.gr, peak.RAD21)
length(unique(queryHits(overlap.body.ctcf)))
length(unique(queryHits(overlap.body.rad21)))
length(unique(queryHits(overlap.anchor.ctcf)))
length(unique(queryHits(overlap.anchor.rad21)))
## Checking overlap with loop anchor (PE-PE)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)
overlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)
overlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)
overlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)
length(unique(subjectHits(overlap.body.upno)))
length(unique(subjectHits(overlap.body.down)))
length(unique(subjectHits(overlap.anchor.upno)))
length(unique(subjectHits(overlap.anchor.down)))
## Checking overlap with loop anchor (ALL)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)
overlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)
overlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)
overlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)
length(unique(subjectHits(overlap.body.upno)))
length(unique(subjectHits(overlap.body.down)))
length(unique(subjectHits(overlap.anchor.upno)))
length(unique(subjectHits(overlap.anchor.down)))
## Checking overlap with loop anchor (Structure)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)
overlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)
overlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)
overlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)
length(unique(subjectHits(overlap.body.upno)))
length(unique(subjectHits(overlap.body.down)))
length(unique(subjectHits(overlap.anchor.upno)))
length(unique(subjectHits(overlap.anchor.down)))
length(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100
length(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100
length(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100
length(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100
## Checking overlap with loop anchor (RelaxedReg)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)
overlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)
overlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)
overlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)
length(unique(subjectHits(overlap.body.upno)))
length(unique(subjectHits(overlap.body.down)))
length(unique(subjectHits(overlap.anchor.upno)))
length(unique(subjectHits(overlap.anchor.down)))
length(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100
length(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100
length(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100
length(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nflankSize <- 1\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\, \\ensembl\\)\n\nbed.1 <- gene.tb %>% dplyr::filter(ensembl %in% group1) %>% dplyr::select(c(1, 2, 3))\nbed.2 <- gene.tb %>% dplyr::filter(ensembl %in% group2) %>% dplyr::select(c(1, 2, 3))\n\n\ndata <- fread(here(\\../..\\, \\result\\, \\stripenn\\, \\result_filtered.tsv\\))\n\nggplot(data, aes(x = width)) + geom_histogram() + theme_classic() +\n scale_x_continuous(labels = label_kb_mb)\n\n\n## Checking overlap with gene TSS\n\ntss <- fread(here(refDir, \\mm10_GRCm38.p6_TSS2.5kb.bed\\))\n\ntss.group1 <- tss %>% dplyr::filter(V6 %in% group1) %>% dplyr::select(V1, V2, V3)\ncolnames(tss.group1) <- c(\\chr\\, \\start\\, \\end\\)\ntss.group1.gr <- makeGRangesFromDataFrame(tss.group1)\n\ntss.group2 <- tss %>% dplyr::filter(V6 %in% group2) %>% dplyr::select(V1, V2, V3)\ncolnames(tss.group2) <- c(\\chr\\, \\start\\, \\end\\)\ntss.group2.gr <- makeGRangesFromDataFrame(tss.group2)\n\n## Anchor of stripes\ntemp <- data %>% dplyr::select(chr, pos1, pos2)\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n\nstripeAnchor.gr <- makeGRangesFromDataFrame(temp)\n\noverlap.group1 <- findOverlaps(tss.group1.gr, stripeAnchor.gr)\noverlap.group2 <- findOverlaps(tss.group2.gr, stripeAnchor.gr)\n\nlength(unique(queryHits(overlap.group1)))\nlength(unique(queryHits(overlap.group2)))\n\n\n## body of stripes\ntemp <- data %>% dplyr::select(chr, pos3, pos4)\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n\nstripeBody.gr <- makeGRangesFromDataFrame(temp)\n\noverlap.group1 <- findOverlaps(tss.group1.gr, stripeBody.gr)\noverlap.group2 <- findOverlaps(tss.group2.gr, stripeBody.gr)\n\nlength(unique(queryHits(overlap.group1)))\nlength(unique(queryHits(overlap.group2)))\n\n## Checking RAD21/CTCF presence at anchor\noverlap.body.ctcf <- findOverlaps(stripeBody.gr, peak.CTCF)\noverlap.body.rad21 <- findOverlaps(stripeBody.gr, peak.RAD21)\noverlap.anchor.ctcf <- findOverlaps(stripeAnchor.gr, peak.CTCF)\noverlap.anchor.rad21 <- findOverlaps(stripeAnchor.gr, peak.RAD21)\n\nlength(unique(queryHits(overlap.body.ctcf)))\nlength(unique(queryHits(overlap.body.rad21)))\n\nlength(unique(queryHits(overlap.anchor.ctcf)))\nlength(unique(queryHits(overlap.anchor.rad21)))\n  \n## Checking overlap with loop anchor (PE-PE)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\n## Checking overlap with loop anchor (ALL)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\n## Checking overlap with loop anchor (Structure)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\nlength(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100\nlength(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100\n\n\n## Checking overlap with loop anchor (RelaxedReg)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\nlength(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100\nlength(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100\n\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nflankSize <- 1\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\, \\ensembl\\)\n\nbed.1 <- gene.tb %>% dplyr::filter(ensembl %in% group1) %>% dplyr::select(c(1, 2, 3))\nbed.2 <- gene.tb %>% dplyr::filter(ensembl %in% group2) %>% dplyr::select(c(1, 2, 3))\n\n\ndata <- fread(here(\\../..\\, \\result\\, \\stripenn\\, \\result_filtered.tsv\\))\n\nggplot(data, aes(x = width)) + geom_histogram() + theme_classic() +\n scale_x_continuous(labels = label_kb_mb)\n\n\n## Checking overlap with gene TSS\n\ntss <- fread(here(refDir, \\mm10_GRCm38.p6_TSS2.5kb.bed\\))\n\ntss.group1 <- tss %>% dplyr::filter(V6 %in% group1) %>% dplyr::select(V1, V2, V3)\ncolnames(tss.group1) <- c(\\chr\\, \\start\\, \\end\\)\ntss.group1.gr <- makeGRangesFromDataFrame(tss.group1)\n\ntss.group2 <- tss %>% dplyr::filter(V6 %in% group2) %>% dplyr::select(V1, V2, V3)\ncolnames(tss.group2) <- c(\\chr\\, \\start\\, \\end\\)\ntss.group2.gr <- makeGRangesFromDataFrame(tss.group2)\n\n## Anchor of stripes\ntemp <- data %>% dplyr::select(chr, pos1, pos2)\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n\nstripeAnchor.gr <- makeGRangesFromDataFrame(temp)\n\noverlap.group1 <- findOverlaps(tss.group1.gr, stripeAnchor.gr)\noverlap.group2 <- findOverlaps(tss.group2.gr, stripeAnchor.gr)\n\nlength(unique(queryHits(overlap.group1)))\nlength(unique(queryHits(overlap.group2)))\n\n\n## body of stripes\ntemp <- data %>% dplyr::select(chr, pos3, pos4)\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n\nstripeBody.gr <- makeGRangesFromDataFrame(temp)\n\noverlap.group1 <- findOverlaps(tss.group1.gr, stripeBody.gr)\noverlap.group2 <- findOverlaps(tss.group2.gr, stripeBody.gr)\n\nlength(unique(queryHits(overlap.group1)))\nlength(unique(queryHits(overlap.group2)))\n\n## Checking RAD21/CTCF presence at anchor\noverlap.body.ctcf <- findOverlaps(stripeBody.gr, peak.CTCF)\noverlap.body.rad21 <- findOverlaps(stripeBody.gr, peak.RAD21)\noverlap.anchor.ctcf <- findOverlaps(stripeAnchor.gr, peak.CTCF)\noverlap.anchor.rad21 <- findOverlaps(stripeAnchor.gr, peak.RAD21)\n\nlength(unique(queryHits(overlap.body.ctcf)))\nlength(unique(queryHits(overlap.body.rad21)))\n\nlength(unique(queryHits(overlap.anchor.ctcf)))\nlength(unique(queryHits(overlap.anchor.rad21)))\n  \n## Checking overlap with loop anchor (PE-PE)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\n## Checking overlap with loop anchor (ALL)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\n## Checking overlap with loop anchor (Structure)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_structure_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\nlength(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100\nlength(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100\n\n\n## Checking overlap with loop anchor (RelaxedReg)\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nanchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\nanchor.down <- extractAnchor(loop.down)\n\noverlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)\noverlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)\noverlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)\noverlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)\n\nlength(unique(subjectHits(overlap.body.upno)))\nlength(unique(subjectHits(overlap.body.down)))\nlength(unique(subjectHits(overlap.anchor.upno)))\nlength(unique(subjectHits(overlap.anchor.down)))\n\n\nlength(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100\nlength(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100\nlength(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100\n\n\n\n\n```\n```"} -->
```r
```r
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
flankSize <- 1
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\)
bed.1 <- gene.tb %>% dplyr::filter(ensembl %in% group1) %>% dplyr::select(c(1, 2, 3))
bed.2 <- gene.tb %>% dplyr::filter(ensembl %in% group2) %>% dplyr::select(c(1, 2, 3))
data <- fread(here(\../..\, \result\, \stripenn\, \result_filtered.tsv\))
ggplot(data, aes(x = width)) + geom_histogram() + theme_classic() +
scale_x_continuous(labels = label_kb_mb)
## Checking overlap with gene TSS
tss <- fread(here(refDir, \mm10_GRCm38.p6_TSS2.5kb.bed\))
tss.group1 <- tss %>% dplyr::filter(V6 %in% group1) %>% dplyr::select(V1, V2, V3)
colnames(tss.group1) <- c(\chr\, \start\, \end\)
tss.group1.gr <- makeGRangesFromDataFrame(tss.group1)
tss.group2 <- tss %>% dplyr::filter(V6 %in% group2) %>% dplyr::select(V1, V2, V3)
colnames(tss.group2) <- c(\chr\, \start\, \end\)
tss.group2.gr <- makeGRangesFromDataFrame(tss.group2)
## Anchor of stripes
temp <- data %>% dplyr::select(chr, pos1, pos2)
colnames(temp) <- c(\chr\, \start\, \end\)
stripeAnchor.gr <- makeGRangesFromDataFrame(temp)
overlap.group1 <- findOverlaps(tss.group1.gr, stripeAnchor.gr)
overlap.group2 <- findOverlaps(tss.group2.gr, stripeAnchor.gr)
length(unique(queryHits(overlap.group1)))
length(unique(queryHits(overlap.group2)))
## body of stripes
temp <- data %>% dplyr::select(chr, pos3, pos4)
colnames(temp) <- c(\chr\, \start\, \end\)
stripeBody.gr <- makeGRangesFromDataFrame(temp)
overlap.group1 <- findOverlaps(tss.group1.gr, stripeBody.gr)
overlap.group2 <- findOverlaps(tss.group2.gr, stripeBody.gr)
length(unique(queryHits(overlap.group1)))
length(unique(queryHits(overlap.group2)))
## Checking RAD21/CTCF presence at anchor
overlap.body.ctcf <- findOverlaps(stripeBody.gr, peak.CTCF)
overlap.body.rad21 <- findOverlaps(stripeBody.gr, peak.RAD21)
overlap.anchor.ctcf <- findOverlaps(stripeAnchor.gr, peak.CTCF)
overlap.anchor.rad21 <- findOverlaps(stripeAnchor.gr, peak.RAD21)
length(unique(queryHits(overlap.body.ctcf)))
length(unique(queryHits(overlap.body.rad21)))
length(unique(queryHits(overlap.anchor.ctcf)))
length(unique(queryHits(overlap.anchor.rad21)))
## Checking overlap with loop anchor (PE-PE)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)
overlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)
overlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)
overlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)
length(unique(subjectHits(overlap.body.upno)))
length(unique(subjectHits(overlap.body.down)))
length(unique(subjectHits(overlap.anchor.upno)))
length(unique(subjectHits(overlap.anchor.down)))
## Checking overlap with loop anchor (ALL)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)
overlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)
overlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)
overlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)
length(unique(subjectHits(overlap.body.upno)))
length(unique(subjectHits(overlap.body.down)))
length(unique(subjectHits(overlap.anchor.upno)))
length(unique(subjectHits(overlap.anchor.down)))
## Checking overlap with loop anchor (Structure)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)
overlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)
overlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)
overlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)
length(unique(subjectHits(overlap.body.upno)))
length(unique(subjectHits(overlap.body.down)))
length(unique(subjectHits(overlap.anchor.upno)))
length(unique(subjectHits(overlap.anchor.down)))
length(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100
length(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100
length(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100
length(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100
## Checking overlap with loop anchor (RelaxedReg)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_regulatory_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.body.upno <- findOverlaps(stripeBody.gr, anchor.upno)
overlap.body.down <- findOverlaps(stripeBody.gr, anchor.down)
overlap.anchor.upno <- findOverlaps(stripeAnchor.gr, anchor.upno)
overlap.anchor.down <- findOverlaps(stripeAnchor.gr, anchor.down)
length(unique(subjectHits(overlap.body.upno)))
length(unique(subjectHits(overlap.body.down)))
length(unique(subjectHits(overlap.anchor.upno)))
length(unique(subjectHits(overlap.anchor.down)))
length(unique(subjectHits(overlap.body.upno)))/length(anchor.upno)*100
length(unique(subjectHits(overlap.body.down)))/length(anchor.down)*100
length(unique(subjectHits(overlap.anchor.upno)))/length(anchor.upno)*100
length(unique(subjectHits(overlap.anchor.down)))/length(anchor.down)*100
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.28] Checking gene expression levels of binaryGroup
<!-- rnb-text-end -->
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results <- tibble(i = numeric(), upno = numeric(), down = numeric())
for(i in seq(1, 10)){
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.upno <- findOverlaps(anchor.upno, hub.anchor)
overlap.down <- findOverlaps(anchor.down, hub.anchor)
n1 <- length(unique(queryHits(overlap.upno)))
n2 <- length(unique(queryHits(overlap.down)))
perc1 <- round(n1/length(overlap.upno)*100, 2)
perc2 <- round(n2/length(overlap.down)*100, 2)
results <- results %>% add_row(i = i, upno = perc1, down = perc2)
}
results_long <- results %>%
pivot_longer(cols = c(upno, down), names_to = \Group\, values_to = \Percentage\)
results_long$Group <- factor(results_long$Group, levels = c(\upno\, \down\))
# Create the bar plot
ggplot(results_long, aes(x = factor(i), y = Percentage, fill = Group)) +
geom_bar(stat = \identity\, position = \dodge\) +
labs(
title = \All loops\,
x = \Presence of hub anchor with >i connections\,
y = \Percentage\
) +
theme_minimal() +
scale_fill_manual(values = c(\upno\ = \blue\, \down\ = \red\)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ylim(0, 100)
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-->
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```r
```r
results <- tibble(i = numeric(), upno = numeric(), down = numeric())
for(i in seq(1, 10)){
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_all_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.upno <- findOverlaps(anchor.upno, hub.anchor)
overlap.down <- findOverlaps(anchor.down, hub.anchor)
n1 <- length(unique(queryHits(overlap.upno)))
n2 <- length(unique(queryHits(overlap.down)))
perc1 <- round(n1/length(overlap.upno)*100, 2)
perc2 <- round(n2/length(overlap.down)*100, 2)
results <- results %>% add_row(i = i, upno = perc1, down = perc2)
}
results_long <- results %>%
pivot_longer(cols = c(upno, down), names_to = \Group\, values_to = \Percentage\)
results_long$Group <- factor(results_long$Group, levels = c(\upno\, \down\))
# Create the bar plot
ggplot(results_long, aes(x = factor(i), y = Percentage, fill = Group)) +
geom_bar(stat = \identity\, position = \dodge\) +
labs(
title = \All loops\,
x = \Presence of hub anchor with >i connections\,
y = \Percentage\
) +
theme_minimal() +
scale_fill_manual(values = c(\upno\ = \blue\, \down\ = \red\)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ylim(0, 100)
```
```
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### [2.29] Checking overlap with Stripes
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin {"data":"```r\nloop.up <- fread(here(consensusDir, \"chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\"))\nloop.no <- fread(here(consensusDir, \"chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\"))\nloop.upno <- (bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \"chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\"))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\"Overlapping\", \"Not_Overlapping\")\n  rownames(contingency_table) <- c(\"All loops\", \"Subset loops\")\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\nname <- \"chromo_cons_annoHierarchy\"\nloop <- fread(here(consensusDir, \"chromo_cons_annoHierarchy_pe-pe.bedpe\"))\n\n\ni <- 2\ndata <- fread(here(refDir, \"Dylan_hub_esc.csv\"))\n\ndata.hub <- data %>% dplyr::filter(all_lcon > i)\ntemp <- data.hub %>% dplyr::select(c(1, 2, 3))\ncolnames(temp) <- c(\"chr\", \"start\", \"end\")\nhub.anchor <- makeGRangesFromDataFrame(temp)\n\n\ntemp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)\nresult.tb <- tibble(loopType = \"UP/NO\",\n                    target = paste0(\"hub_\", i),\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\ntemp <- getSEOverlapFisher(loop, loop.down, hub.anchor)\nresult.tb <- result.tb %>% \n  add_row(loopType = \"DOWN\",\n          target =  paste0(\"hub_\", i),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\nfor(i in c(5)){\n  data <- fread(here(refDir, \"Dylan_hub_esc.csv\"))\n  \n  data.hub <- data %>% dplyr::filter(all_lcon > i)\n  temp <- data.hub %>% dplyr::select(c(1, 2, 3))\n  colnames(temp) <- c(\"chr\", \"start\", \"end\")\n  hub.anchor <- makeGRangesFromDataFrame(temp)\n  \n  \n  temp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)\n  result.tb <- result.tb %>% add_row(loopType = \"UP/NO\",\n                      target = paste0(\"hub_\", i),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  temp <- getSEOverlapFisher(loop, loop.down, hub.anchor)\n  result.tb <- result.tb %>% \n    add_row(loopType = \"DOWN\",\n            target =  paste0(\"hub_\", i),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  \n}\n\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \"target\")\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \"target\")\n\n# col_fun <- colorRamp2(c(0, 1, 2), \n#                       c(\"blue\", \"white\", \"red\"))\n\ndata$target <- factor(data$target, levels = c(\"hub_5\", \"hub_2\"))\ndata$loopType <- factor(data$loopType, levels = c(\"UP/NO\", \"DOWN\"))\np <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\"#4852A0\", \"white\", \"#CB333A\"),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \"white\", high = \"#CB333A\",\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\n\nfileName <- here(figDir, \"heatmap_hub_enrichment_dotplot_all\")\nwidth <- panelSize(1.65)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# # png(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\n# # print(p)\n# # dev.off()\nsvglite(paste0(fileName, \".svg\"),  height = height, width = width)\nprint(p)\ndev.off()\n```"} -->\n\n```r\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nloop.upno <- (bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\\Overlapping\\, \\Not_Overlapping\\)\n  rownames(contingency_table) <- c(\\All loops\\, \\Subset loops\\)\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\nname <- \\chromo_cons_annoHierarchy\\\nloop <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\))\n\n\ni <- 2\ndata <- fread(here(refDir, \\Dylan_hub_esc.csv\\))\n\ndata.hub <- data %>% dplyr::filter(all_lcon > i)\ntemp <- data.hub %>% dplyr::select(c(1, 2, 3))\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\nhub.anchor <- makeGRangesFromDataFrame(temp)\n\n\ntemp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)\nresult.tb <- tibble(loopType = \\UP/NO\\,\n                    target = paste0(\\hub_\\, i),\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\ntemp <- getSEOverlapFisher(loop, loop.down, hub.anchor)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target =  paste0(\\hub_\\, i),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\nfor(i in c(5)){\n  data <- fread(here(refDir, \\Dylan_hub_esc.csv\\))\n  \n  data.hub <- data %>% dplyr::filter(all_lcon > i)\n  temp <- data.hub %>% dplyr::select(c(1, 2, 3))\n  colnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n  hub.anchor <- makeGRangesFromDataFrame(temp)\n  \n  \n  temp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)\n  result.tb <- result.tb %>% add_row(loopType = \\UP/NO\\,\n                      target = paste0(\\hub_\\, i),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  temp <- getSEOverlapFisher(loop, loop.down, hub.anchor)\n  result.tb <- result.tb %>% \n    add_row(loopType = \\DOWN\\,\n            target =  paste0(\\hub_\\, i),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  \n}\n\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\n# col_fun <- colorRamp2(c(0, 1, 2), \n#                       c(\\blue\\, \\white\\, \\red\\))\n\ndata$target <- factor(data$target, levels = c(\\hub_5\\, \\hub_2\\))\ndata$loopType <- factor(data$loopType, levels = c(\\UP/NO\\, \\DOWN\\))\np <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\n\nfileName <- here(figDir, \\heatmap_hub_enrichment_dotplot_all\\)\nwidth <- panelSize(1.65)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# # png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# # print(p)\n# # dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
loop.upno <- (bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
getOverlapLoopNum <- function(loop, peak){
anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))
anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))
a <- queryHits(findOverlaps(anchor1, peak))
b <- queryHits(findOverlaps(anchor2, peak))
return(length(unique(c(a, b))))
}
getSEOverlapFisher <- function(allLoop, subsetLoop, peak){
all.overlap <- getOverlapLoopNum(allLoop, peak)
all.notOverlap <- nrow(allLoop) - all.overlap
subset.overlap <- getOverlapLoopNum(subsetLoop, peak)
subset.notOverlap <- nrow(subsetLoop) - subset.overlap
contingency_table <- matrix(c(subset.overlap, subset.notOverlap,
all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)
colnames(contingency_table) <- c(\Overlapping\, \Not_Overlapping\)
rownames(contingency_table) <- c(\All loops\, \Subset loops\)
# Perform Fisher's Exact Test
fisher_test_result <- fisher.test(contingency_table)
return(fisher_test_result)
}
name <- \chromo_cons_annoHierarchy\
loop <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
i <- 2
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
temp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)
result.tb <- tibble(loopType = \UP/NO\,
target = paste0(\hub_\, i),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, hub.anchor)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = paste0(\hub_\, i),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
for(i in c(5)){
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
temp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)
result.tb <- result.tb %>% add_row(loopType = \UP/NO\,
target = paste0(\hub_\, i),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, hub.anchor)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = paste0(\hub_\, i),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
}
library(circlize)
data <- result.tb
heatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%
pivot_wider(names_from = loopType, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%
pivot_wider(names_from = loopType, values_from = pvalue) %>%
column_to_rownames(var = \target\)
# col_fun <- colorRamp2(c(0, 1, 2),
# c(\blue\, \white\, \red\))
data$target <- factor(data$target, levels = c(\hub_5\, \hub_2\))
data$loopType <- factor(data$loopType, levels = c(\UP/NO\, \DOWN\))
p <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
scale_fill_gradientn(colors = c(\#4852A0\, \white\, \#CB333A\), # Define gradient colors
values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5),
#low = \white\, high = \#CB333A\,
# limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) + labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \heatmap_hub_enrichment_dotplot_all\)
width <- panelSize(1.65)*mmToInch
height <- panelSize(1.1)*mmToInch
# # png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# # print(p)
# # dev.off()
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nloop.upno <- (bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\\Overlapping\\, \\Not_Overlapping\\)\n  rownames(contingency_table) <- c(\\All loops\\, \\Subset loops\\)\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\nname <- \\chromo_cons_annoHierarchy\\\nloop <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\))\n\n\ni <- 2\ndata <- fread(here(refDir, \\Dylan_hub_esc.csv\\))\n\ndata.hub <- data %>% dplyr::filter(all_lcon > i)\ntemp <- data.hub %>% dplyr::select(c(1, 2, 3))\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\nhub.anchor <- makeGRangesFromDataFrame(temp)\n\n\ntemp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)\nresult.tb <- tibble(loopType = \\UP/NO\\,\n                    target = paste0(\\hub_\\, i),\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\ntemp <- getSEOverlapFisher(loop, loop.down, hub.anchor)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target =  paste0(\\hub_\\, i),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\nfor(i in c(5)){\n  data <- fread(here(refDir, \\Dylan_hub_esc.csv\\))\n  \n  data.hub <- data %>% dplyr::filter(all_lcon > i)\n  temp <- data.hub %>% dplyr::select(c(1, 2, 3))\n  colnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n  hub.anchor <- makeGRangesFromDataFrame(temp)\n  \n  \n  temp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)\n  result.tb <- result.tb %>% add_row(loopType = \\UP/NO\\,\n                      target = paste0(\\hub_\\, i),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  temp <- getSEOverlapFisher(loop, loop.down, hub.anchor)\n  result.tb <- result.tb %>% \n    add_row(loopType = \\DOWN\\,\n            target =  paste0(\\hub_\\, i),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  \n}\n\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\n# col_fun <- colorRamp2(c(0, 1, 2), \n#                       c(\\blue\\, \\white\\, \\red\\))\n\ndata$target <- factor(data$target, levels = c(\\hub_5\\, \\hub_2\\))\ndata$loopType <- factor(data$loopType, levels = c(\\UP/NO\\, \\DOWN\\))\np <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\n\nfileName <- here(figDir, \\heatmap_hub_enrichment_dotplot_all\\)\nwidth <- panelSize(1.65)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# # png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# # print(p)\n# # dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\))\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\))\nloop.upno <- (bind_rows(loop.up, loop.no))\n\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\))\n\ngetOverlapLoopNum <- function(loop, peak){\n  anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))\n  anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))\n  a <- queryHits(findOverlaps(anchor1, peak))\n  b <- queryHits(findOverlaps(anchor2, peak))\n  return(length(unique(c(a, b))))\n}\n\ngetSEOverlapFisher <- function(allLoop, subsetLoop, peak){\n  all.overlap <- getOverlapLoopNum(allLoop, peak)\n  all.notOverlap <- nrow(allLoop) - all.overlap\n  \n  subset.overlap <- getOverlapLoopNum(subsetLoop, peak)\n  subset.notOverlap <- nrow(subsetLoop) - subset.overlap\n  \n  contingency_table <- matrix(c(subset.overlap, subset.notOverlap,\n                                all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)\n  colnames(contingency_table) <- c(\\Overlapping\\, \\Not_Overlapping\\)\n  rownames(contingency_table) <- c(\\All loops\\, \\Subset loops\\)\n  \n  # Perform Fisher's Exact Test\n  fisher_test_result <- fisher.test(contingency_table)\n  return(fisher_test_result)\n}\n\nname <- \\chromo_cons_annoHierarchy\\\nloop <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe.bedpe\\))\n\n\ni <- 2\ndata <- fread(here(refDir, \\Dylan_hub_esc.csv\\))\n\ndata.hub <- data %>% dplyr::filter(all_lcon > i)\ntemp <- data.hub %>% dplyr::select(c(1, 2, 3))\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\nhub.anchor <- makeGRangesFromDataFrame(temp)\n\n\ntemp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)\nresult.tb <- tibble(loopType = \\UP/NO\\,\n                    target = paste0(\\hub_\\, i),\n                    pvalue = temp$p.value,\n                    oddsRatio = temp$estimate)\ntemp <- getSEOverlapFisher(loop, loop.down, hub.anchor)\nresult.tb <- result.tb %>% \n  add_row(loopType = \\DOWN\\,\n          target =  paste0(\\hub_\\, i),\n          pvalue = temp$p.value,\n          oddsRatio = temp$estimate)\n\n\nfor(i in c(5)){\n  data <- fread(here(refDir, \\Dylan_hub_esc.csv\\))\n  \n  data.hub <- data %>% dplyr::filter(all_lcon > i)\n  temp <- data.hub %>% dplyr::select(c(1, 2, 3))\n  colnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\n  hub.anchor <- makeGRangesFromDataFrame(temp)\n  \n  \n  temp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)\n  result.tb <- result.tb %>% add_row(loopType = \\UP/NO\\,\n                      target = paste0(\\hub_\\, i),\n                      pvalue = temp$p.value,\n                      oddsRatio = temp$estimate)\n  temp <- getSEOverlapFisher(loop, loop.down, hub.anchor)\n  result.tb <- result.tb %>% \n    add_row(loopType = \\DOWN\\,\n            target =  paste0(\\hub_\\, i),\n            pvalue = temp$p.value,\n            oddsRatio = temp$estimate)\n  \n  \n}\n\nlibrary(circlize)\ndata <- result.tb\nheatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%\npivot_wider(names_from = loopType, values_from = oddsRatio) %>%\n  column_to_rownames(var = \\target\\)\n\npvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%\n  pivot_wider(names_from = loopType, values_from = pvalue) %>%\n  column_to_rownames(var = \\target\\)\n\n# col_fun <- colorRamp2(c(0, 1, 2), \n#                       c(\\blue\\, \\white\\, \\red\\))\n\ndata$target <- factor(data$target, levels = c(\\hub_5\\, \\hub_2\\))\ndata$loopType <- factor(data$loopType, levels = c(\\UP/NO\\, \\DOWN\\))\np <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +\n  geom_point(shape = 21,        # Ensures a point with an outline\n             stroke = 1*ptToMM      # Line width for the border\n  ) + theme_bw() + \n  scale_size_continuous(range = c(1, 3)) +  # Set min and max point sizes here\n  scale_fill_gradientn(colors = c(\\#4852A0\\, \\white\\, \\#CB333A\\),  # Define gradient colors\n                      values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5), \n                      #low = \\white\\, high = \\#CB333A\\,\n                      #                  limits = c(1, 3),\n                      oob = scales::squish, # Define gradient colors\n                      guide = guide_colorbar(\n                        barwidth = 1.5/5.08,  # Adjust width of the color bar\n                        barheight = 15/5.08   # Adjust height of the color bar\n                      )\n  ) + labs(x = NULL, y = NULL)  +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )\n\n\nfileName <- here(figDir, \\heatmap_hub_enrichment_dotplot_all\\)\nwidth <- panelSize(1.65)*mmToInch\nheight <- panelSize(1.1)*mmToInch\n# # png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n# # print(p)\n# # dev.off()\nsvglite(paste0(fileName, \\.svg\\),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
loop.upno <- (bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
getOverlapLoopNum <- function(loop, peak){
anchor1 <- GRanges(seqnames = loop$V1, ranges = IRanges(start = loop$V2, end = loop$V3))
anchor2 <- GRanges(seqnames = loop$V4, ranges = IRanges(start = loop$V5, end = loop$V6))
a <- queryHits(findOverlaps(anchor1, peak))
b <- queryHits(findOverlaps(anchor2, peak))
return(length(unique(c(a, b))))
}
getSEOverlapFisher <- function(allLoop, subsetLoop, peak){
all.overlap <- getOverlapLoopNum(allLoop, peak)
all.notOverlap <- nrow(allLoop) - all.overlap
subset.overlap <- getOverlapLoopNum(subsetLoop, peak)
subset.notOverlap <- nrow(subsetLoop) - subset.overlap
contingency_table <- matrix(c(subset.overlap, subset.notOverlap,
all.overlap, all.notOverlap), nrow = 2, byrow = TRUE)
colnames(contingency_table) <- c(\Overlapping\, \Not_Overlapping\)
rownames(contingency_table) <- c(\All loops\, \Subset loops\)
# Perform Fisher's Exact Test
fisher_test_result <- fisher.test(contingency_table)
return(fisher_test_result)
}
name <- \chromo_cons_annoHierarchy\
loop <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe.bedpe\))
i <- 2
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
temp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)
result.tb <- tibble(loopType = \UP/NO\,
target = paste0(\hub_\, i),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, hub.anchor)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = paste0(\hub_\, i),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
for(i in c(5)){
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
temp <- getSEOverlapFisher(loop, loop.upno, hub.anchor)
result.tb <- result.tb %>% add_row(loopType = \UP/NO\,
target = paste0(\hub_\, i),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
temp <- getSEOverlapFisher(loop, loop.down, hub.anchor)
result.tb <- result.tb %>%
add_row(loopType = \DOWN\,
target = paste0(\hub_\, i),
pvalue = temp$p.value,
oddsRatio = temp$estimate)
}
library(circlize)
data <- result.tb
heatmap_data <- data %>% dplyr::select(target, loopType, oddsRatio) %>%
pivot_wider(names_from = loopType, values_from = oddsRatio) %>%
column_to_rownames(var = \target\)
pvalue_data <- data %>% dplyr::select(target, loopType, pvalue) %>%
pivot_wider(names_from = loopType, values_from = pvalue) %>%
column_to_rownames(var = \target\)
# col_fun <- colorRamp2(c(0, 1, 2),
# c(\blue\, \white\, \red\))
data$target <- factor(data$target, levels = c(\hub_5\, \hub_2\))
data$loopType <- factor(data$loopType, levels = c(\UP/NO\, \DOWN\))
p <- ggplot(data, aes(x = loopType, y = target, size = -log10(pvalue), fill = oddsRatio)) +
geom_point(shape = 21, # Ensures a point with an outline
stroke = 1*ptToMM # Line width for the border
) + theme_bw() +
scale_size_continuous(range = c(1, 3)) + # Set min and max point sizes here
scale_fill_gradientn(colors = c(\#4852A0\, \white\, \#CB333A\), # Define gradient colors
values = scales::rescale(c(0.5, 1, 1.5)), limits = c(0.5, 1.5),
#low = \white\, high = \#CB333A\,
# limits = c(1, 3),
oob = scales::squish, # Define gradient colors
guide = guide_colorbar(
barwidth = 1.5/5.08, # Adjust width of the color bar
barheight = 15/5.08 # Adjust height of the color bar
)
) + labs(x = NULL, y = NULL) +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- here(figDir, \heatmap_hub_enrichment_dotplot_all\)
width <- panelSize(1.65)*mmToInch
height <- panelSize(1.1)*mmToInch
# # png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
# # print(p)
# # dev.off()
svglite(paste0(fileName, \.svg\), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.30] Hubs from Dylan Paper
#### All loops
<!-- rnb-text-end -->
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results <- tibble(i = numeric(), upno = numeric(), down = numeric())
for(i in seq(1, 10)){
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.upno <- findOverlaps(anchor.upno, hub.anchor)
overlap.down <- findOverlaps(anchor.down, hub.anchor)
n1 <- length(unique(queryHits(overlap.upno)))
n2 <- length(unique(queryHits(overlap.down)))
perc1 <- round(n1/length(overlap.upno)*100, 2)
perc2 <- round(n2/length(overlap.down)*100, 2)
results <- results %>% add_row(i = i, upno = perc1, down = perc2)
}
results_long <- results %>%
pivot_longer(cols = c(upno, down), names_to = \Group\, values_to = \Percentage\)
results_long$Group <- factor(results_long$Group, levels = c(\upno\, \down\))
# Create the bar plot
ggplot(results_long, aes(x = factor(i), y = Percentage, fill = Group)) +
geom_bar(stat = \identity\, position = \dodge\) +
labs(
title = \PE-PE loops\,
x = \Presence of hub anchor with >i connections\,
y = \Percentage\
) +
theme_minimal() +
scale_fill_manual(values = c(\upno\ = \blue\, \down\ = \red\)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ylim(0, 100)
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-->
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```r
```r
results <- tibble(i = numeric(), upno = numeric(), down = numeric())
for(i in seq(1, 10)){
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.upno <- findOverlaps(anchor.upno, hub.anchor)
overlap.down <- findOverlaps(anchor.down, hub.anchor)
n1 <- length(unique(queryHits(overlap.upno)))
n2 <- length(unique(queryHits(overlap.down)))
perc1 <- round(n1/length(overlap.upno)*100, 2)
perc2 <- round(n2/length(overlap.down)*100, 2)
results <- results %>% add_row(i = i, upno = perc1, down = perc2)
}
results_long <- results %>%
pivot_longer(cols = c(upno, down), names_to = \Group\, values_to = \Percentage\)
results_long$Group <- factor(results_long$Group, levels = c(\upno\, \down\))
# Create the bar plot
ggplot(results_long, aes(x = factor(i), y = Percentage, fill = Group)) +
geom_bar(stat = \identity\, position = \dodge\) +
labs(
title = \PE-PE loops\,
x = \Presence of hub anchor with >i connections\,
y = \Percentage\
) +
theme_minimal() +
scale_fill_manual(values = c(\upno\ = \blue\, \down\ = \red\)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ylim(0, 100)
```
```
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##### Fishers
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results <- tibble(i = numeric(), upno = numeric(), down = numeric())
for(i in seq(1, 10)){
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.upno <- findOverlaps(anchor.upno, hub.anchor)
overlap.down <- findOverlaps(anchor.down, hub.anchor)
n1 <- length(unique(queryHits(overlap.upno)))
n2 <- length(unique(queryHits(overlap.down)))
perc1 <- round(n1/length(overlap.upno)*100, 2)
perc2 <- round(n2/length(overlap.down)*100, 2)
results <- results %>% add_row(i = i, upno = perc1, down = perc2)
}
results_long <- results %>%
pivot_longer(cols = c(upno, down), names_to = \Group\, values_to = \Percentage\)
results_long$Group <- factor(results_long$Group, levels = c(\upno\, \down\))
# Create the bar plot
ggplot(results_long, aes(x = factor(i), y = Percentage, fill = Group)) +
geom_bar(stat = \identity\, position = \dodge\) +
labs(
title = \Structure loops\,
x = \Presence of hub anchor with >i connections\,
y = \Percentage\
) +
theme_minimal() +
scale_fill_manual(values = c(\upno\ = \blue\, \down\ = \red\)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ylim(0, 100)
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<!-- rnb-output-begin 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-->
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```r
```r
results <- tibble(i = numeric(), upno = numeric(), down = numeric())
for(i in seq(1, 10)){
data <- fread(here(refDir, \Dylan_hub_esc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_UP_diff0.2.bedpe\))
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_NO_diff0.2.bedpe\))
anchor.upno <- extractAnchor(bind_rows(loop.up, loop.no))
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_structure_dTAGvsDMSO_DOWN_diff0.2.bedpe\))
anchor.down <- extractAnchor(loop.down)
overlap.upno <- findOverlaps(anchor.upno, hub.anchor)
overlap.down <- findOverlaps(anchor.down, hub.anchor)
n1 <- length(unique(queryHits(overlap.upno)))
n2 <- length(unique(queryHits(overlap.down)))
perc1 <- round(n1/length(overlap.upno)*100, 2)
perc2 <- round(n2/length(overlap.down)*100, 2)
results <- results %>% add_row(i = i, upno = perc1, down = perc2)
}
results_long <- results %>%
pivot_longer(cols = c(upno, down), names_to = \Group\, values_to = \Percentage\)
results_long$Group <- factor(results_long$Group, levels = c(\upno\, \down\))
# Create the bar plot
ggplot(results_long, aes(x = factor(i), y = Percentage, fill = Group)) +
geom_bar(stat = \identity\, position = \dodge\) +
labs(
title = \Structure loops\,
x = \Presence of hub anchor with >i connections\,
y = \Percentage\
) +
theme_minimal() +
scale_fill_manual(values = c(\upno\ = \blue\, \down\ = \red\)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ylim(0, 100)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### PE-PE loops
<!-- rnb-text-end -->
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results <- tibble(i = numeric(), perc1 = numeric(), perc2 = numeric())
for(i in seq(1, 10)){
data <- fread(here(refDir, \Dylan_hub_episc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
## Checking overlap with gene TSS
tss <- fread(here(refDir, \mm10_GRCm38.p6_TSS2.5kb.bed\))
tss.group1 <- tss %>% dplyr::filter(V6 %in% group1) %>% dplyr::select(V1, V2, V3)
colnames(tss.group1) <- c(\chr\, \start\, \end\)
tss.group1.gr <- makeGRangesFromDataFrame(tss.group1)
tss.group2 <- tss %>% dplyr::filter(V6 %in% group2) %>% dplyr::select(V1, V2, V3)
colnames(tss.group2) <- c(\chr\, \start\, \end\)
tss.group2.gr <- makeGRangesFromDataFrame(tss.group2)
overlap.group1 <- findOverlaps(tss.group1.gr, hub.anchor)
overlap.group2 <- findOverlaps(tss.group2.gr, hub.anchor)
n1 <- length(unique(queryHits(overlap.group1)))
n2 <- length(unique(queryHits(overlap.group2)))
perc1 <- round(n1/nrow(tss.group1)*100, 2)
perc2 <- round(n2/nrow(tss.group2)*100, 2)
results <- results %>% add_row(i = i, perc1 = perc1, perc2 = perc2)
}
results_long <- results %>%
pivot_longer(cols = c(perc1, perc2), names_to = \Group\, values_to = \Percentage\)
# Create the bar plot
ggplot(results_long, aes(x = factor(i), y = Percentage, fill = Group)) +
geom_bar(stat = \identity\, position = \dodge\) +
labs(
title = \Percentage by 'i' for perc1 and perc2\,
x = \Presence of hub anchor with >i connections\,
y = \Percentage\
) +
theme_minimal() +
scale_fill_manual(values = c(\perc1\ = \blue\, \perc2\ = \red\)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
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-->
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```r
```r
results <- tibble(i = numeric(), perc1 = numeric(), perc2 = numeric())
for(i in seq(1, 10)){
data <- fread(here(refDir, \Dylan_hub_episc.csv\))
data.hub <- data %>% dplyr::filter(all_lcon > i)
temp <- data.hub %>% dplyr::select(c(1, 2, 3))
colnames(temp) <- c(\chr\, \start\, \end\)
hub.anchor <- makeGRangesFromDataFrame(temp)
## Checking overlap with gene TSS
tss <- fread(here(refDir, \mm10_GRCm38.p6_TSS2.5kb.bed\))
tss.group1 <- tss %>% dplyr::filter(V6 %in% group1) %>% dplyr::select(V1, V2, V3)
colnames(tss.group1) <- c(\chr\, \start\, \end\)
tss.group1.gr <- makeGRangesFromDataFrame(tss.group1)
tss.group2 <- tss %>% dplyr::filter(V6 %in% group2) %>% dplyr::select(V1, V2, V3)
colnames(tss.group2) <- c(\chr\, \start\, \end\)
tss.group2.gr <- makeGRangesFromDataFrame(tss.group2)
overlap.group1 <- findOverlaps(tss.group1.gr, hub.anchor)
overlap.group2 <- findOverlaps(tss.group2.gr, hub.anchor)
n1 <- length(unique(queryHits(overlap.group1)))
n2 <- length(unique(queryHits(overlap.group2)))
perc1 <- round(n1/nrow(tss.group1)*100, 2)
perc2 <- round(n2/nrow(tss.group2)*100, 2)
results <- results %>% add_row(i = i, perc1 = perc1, perc2 = perc2)
}
results_long <- results %>%
pivot_longer(cols = c(perc1, perc2), names_to = \Group\, values_to = \Percentage\)
# Create the bar plot
ggplot(results_long, aes(x = factor(i), y = Percentage, fill = Group)) +
geom_bar(stat = \identity\, position = \dodge\) +
labs(
title = \Percentage by 'i' for perc1 and perc2\,
x = \Presence of hub anchor with >i connections\,
y = \Percentage\
) +
theme_minimal() +
scale_fill_manual(values = c(\perc1\ = \blue\, \perc2\ = \red\)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```
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#### Str loops
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nprocess_group <- function(group_name, gene_data, bins) {\n  # Filter for the specific group\n  TSS_group <- makeGRangesFromDataFrame(\n    gene_data %>% filter(ensembl %in% get(group_name)), \n    keep.extra.columns = TRUE\n  )\n  \n  # Process each TSS in the group\n  all_results <- map_dfr(seq_len(length(TSS_group)), function(i) {\n    # Find overlapping bins\n    overlapping_bins <- findOverlaps(TSS_group[i], bins)\n    bins_near_tss <- as_tibble(bins[subjectHits(overlapping_bins)])\n    \n    # Calculate distance bin\n    TSScenter <- (as_tibble(TSS_group[i]) %>%\n                    mutate(center = (start + end) / 2))$center\n    centerBinStart <- floor(TSScenter / 10e3) * 10e3 + 1\n    bins_near_tss <- bins_near_tss %>%\n      mutate(distanceBin = abs((start - centerBinStart)) / 10e3)\n    \n    # Summarize results\n    result <- bins_near_tss %>%\n      group_by(distanceBin) %>%\n      summarise(mean_peak_counts = mean(peak_counts, na.rm = TRUE), .groups = \\drop\\) %>%\n      mutate(gene = TSS_group[i]$ensembl)\n    return(result)\n  })\n  \n  # Calculate mean and SD for the group\n  mean_data <- all_results %>%\n    group_by(distanceBin) %>%\n    summarise(mean_peak_counts = mean(mean_peak_counts, na.rm = TRUE))\n  \n  sd_data <- all_results %>%\n    group_by(distanceBin) %>%\n    summarise(sd_peak_counts = sd(mean_peak_counts, na.rm = TRUE))\n  \n  # Join and add group name\n  summary_data <- left_join(mean_data, sd_data, by = \\distanceBin\\) %>%\n    mutate(group = group_name)\n  \n  return(summary_data)\n}\n\n##########################################################\n\n\n\n## Import peak\nrefDir <- here(\\../..\\, \\reference\\)\n#peak.H3K27ac <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\npeak.H3K27ac <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\n#peak.H3K27ac <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n\n## Import 10kb bin\ntemp <- fread(here(refDir, \\mm10.bin.10kb.bed\\))\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\ntemp <- temp %>% dplyr::mutate(start = start+1)\nbins.10kb <- makeGRangesFromDataFrame(temp)\n\n## Count overlap\ncounts <- countOverlaps(bins.10kb, peak.H3K27ac)\nmcols(bins.10kb)$peak_counts <- counts\n\n## Getting TSS\nflankSize <- 1e6\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\, \\ensembl\\)\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n#TSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)\n#TSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)\n\n##########################################################\n# List of groups to process\ngroups <- c(\\gene.group1\\, \\gene.group2\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\gene.group1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\gene.group2\\)\n\n# Plot the results\np <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 0.5) +\n  labs(\n    x = \\Distance bin (10kb)\\,\n    y = \\H3K4me3 peak count\\\n  ) +\n  theme_classic() +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    legend.position = c(0.8, 0.8)  # Moves legend inside the plot (x, y relative coordinates)\n  ) +\n  guides(\n    fill = guide_legend(\n      keywidth = 0.2,  # Adjust the width of the legend keys\n      keyheight = 0.2  # Adjust the height of the legend keys\n    ))+\n  scale_color_manual(values = rev(c(\\#777777\\, \\#F28E2C\\)))\n\n##########################################################\n\nfileName <- paste0(\\binPeakDensity_RAD21_binaryGroup\\)\nwidth <- 32*mmToInch\nheight <- 35*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
process_group <- function(group_name, gene_data, bins) {
# Filter for the specific group
TSS_group <- makeGRangesFromDataFrame(
gene_data %>% filter(ensembl %in% get(group_name)),
keep.extra.columns = TRUE
)
# Process each TSS in the group
all_results <- map_dfr(seq_len(length(TSS_group)), function(i) {
# Find overlapping bins
overlapping_bins <- findOverlaps(TSS_group[i], bins)
bins_near_tss <- as_tibble(bins[subjectHits(overlapping_bins)])
# Calculate distance bin
TSScenter <- (as_tibble(TSS_group[i]) %>%
mutate(center = (start + end) / 2))$center
centerBinStart <- floor(TSScenter / 10e3) * 10e3 + 1
bins_near_tss <- bins_near_tss %>%
mutate(distanceBin = abs((start - centerBinStart)) / 10e3)
# Summarize results
result <- bins_near_tss %>%
group_by(distanceBin) %>%
summarise(mean_peak_counts = mean(peak_counts, na.rm = TRUE), .groups = \drop\) %>%
mutate(gene = TSS_group[i]$ensembl)
return(result)
})
# Calculate mean and SD for the group
mean_data <- all_results %>%
group_by(distanceBin) %>%
summarise(mean_peak_counts = mean(mean_peak_counts, na.rm = TRUE))
sd_data <- all_results %>%
group_by(distanceBin) %>%
summarise(sd_peak_counts = sd(mean_peak_counts, na.rm = TRUE))
# Join and add group name
summary_data <- left_join(mean_data, sd_data, by = \distanceBin\) %>%
mutate(group = group_name)
return(summary_data)
}
##########################################################
## Import peak
refDir <- here(\../..\, \reference\)
#peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
peak.H3K27ac <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
#peak.H3K27ac <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
## Import 10kb bin
temp <- fread(here(refDir, \mm10.bin.10kb.bed\))
colnames(temp) <- c(\chr\, \start\, \end\)
temp <- temp %>% dplyr::mutate(start = start+1)
bins.10kb <- makeGRangesFromDataFrame(temp)
## Count overlap
counts <- countOverlaps(bins.10kb, peak.H3K27ac)
mcols(bins.10kb)$peak_counts <- counts
## Getting TSS
flankSize <- 1e6
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\)
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
#TSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)
#TSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)
##########################################################
# List of groups to process
groups <- c(\gene.group1\, \gene.group2\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \gene.group1\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \gene.group2\)
# Plot the results
p <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 0.5) +
labs(
x = \Distance bin (10kb)\,
y = \H3K4me3 peak count\
) +
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS),
legend.position = c(0.8, 0.8) # Moves legend inside the plot (x, y relative coordinates)
) +
guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))+
scale_color_manual(values = rev(c(\#777777\, \#F28E2C\)))
##########################################################
fileName <- paste0(\binPeakDensity_RAD21_binaryGroup\)
width <- 32*mmToInch
height <- 35*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nprocess_group <- function(group_name, gene_data, bins) {\n  # Filter for the specific group\n  TSS_group <- makeGRangesFromDataFrame(\n    gene_data %>% filter(ensembl %in% get(group_name)), \n    keep.extra.columns = TRUE\n  )\n  \n  # Process each TSS in the group\n  all_results <- map_dfr(seq_len(length(TSS_group)), function(i) {\n    # Find overlapping bins\n    overlapping_bins <- findOverlaps(TSS_group[i], bins)\n    bins_near_tss <- as_tibble(bins[subjectHits(overlapping_bins)])\n    \n    # Calculate distance bin\n    TSScenter <- (as_tibble(TSS_group[i]) %>%\n                    mutate(center = (start + end) / 2))$center\n    centerBinStart <- floor(TSScenter / 10e3) * 10e3 + 1\n    bins_near_tss <- bins_near_tss %>%\n      mutate(distanceBin = abs((start - centerBinStart)) / 10e3)\n    \n    # Summarize results\n    result <- bins_near_tss %>%\n      group_by(distanceBin) %>%\n      summarise(mean_peak_counts = mean(peak_counts, na.rm = TRUE), .groups = \\drop\\) %>%\n      mutate(gene = TSS_group[i]$ensembl)\n    return(result)\n  })\n  \n  # Calculate mean and SD for the group\n  mean_data <- all_results %>%\n    group_by(distanceBin) %>%\n    summarise(mean_peak_counts = mean(mean_peak_counts, na.rm = TRUE))\n  \n  sd_data <- all_results %>%\n    group_by(distanceBin) %>%\n    summarise(sd_peak_counts = sd(mean_peak_counts, na.rm = TRUE))\n  \n  # Join and add group name\n  summary_data <- left_join(mean_data, sd_data, by = \\distanceBin\\) %>%\n    mutate(group = group_name)\n  \n  return(summary_data)\n}\n\n##########################################################\n\n\n\n## Import peak\nrefDir <- here(\\../..\\, \\reference\\)\n#peak.H3K27ac <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\npeak.H3K27ac <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\n#peak.H3K27ac <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n\n## Import 10kb bin\ntemp <- fread(here(refDir, \\mm10.bin.10kb.bed\\))\ncolnames(temp) <- c(\\chr\\, \\start\\, \\end\\)\ntemp <- temp %>% dplyr::mutate(start = start+1)\nbins.10kb <- makeGRangesFromDataFrame(temp)\n\n## Count overlap\ncounts <- countOverlaps(bins.10kb, peak.H3K27ac)\nmcols(bins.10kb)$peak_counts <- counts\n\n## Getting TSS\nflankSize <- 1e6\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\, \\ensembl\\)\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n#TSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)\n#TSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)\n\n##########################################################\n# List of groups to process\ngroups <- c(\\gene.group1\\, \\gene.group2\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\gene.group1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\gene.group2\\)\n\n# Plot the results\np <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 0.5) +\n  labs(\n    x = \\Distance bin (10kb)\\,\n    y = \\H3K4me3 peak count\\\n  ) +\n  theme_classic() +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    legend.position = c(0.8, 0.8)  # Moves legend inside the plot (x, y relative coordinates)\n  ) +\n  guides(\n    fill = guide_legend(\n      keywidth = 0.2,  # Adjust the width of the legend keys\n      keyheight = 0.2  # Adjust the height of the legend keys\n    ))+\n  scale_color_manual(values = rev(c(\\#777777\\, \\#F28E2C\\)))\n\n##########################################################\n\nfileName <- paste0(\\binPeakDensity_RAD21_binaryGroup\\)\nwidth <- 32*mmToInch\nheight <- 35*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
process_group <- function(group_name, gene_data, bins) {
# Filter for the specific group
TSS_group <- makeGRangesFromDataFrame(
gene_data %>% filter(ensembl %in% get(group_name)),
keep.extra.columns = TRUE
)
# Process each TSS in the group
all_results <- map_dfr(seq_len(length(TSS_group)), function(i) {
# Find overlapping bins
overlapping_bins <- findOverlaps(TSS_group[i], bins)
bins_near_tss <- as_tibble(bins[subjectHits(overlapping_bins)])
# Calculate distance bin
TSScenter <- (as_tibble(TSS_group[i]) %>%
mutate(center = (start + end) / 2))$center
centerBinStart <- floor(TSScenter / 10e3) * 10e3 + 1
bins_near_tss <- bins_near_tss %>%
mutate(distanceBin = abs((start - centerBinStart)) / 10e3)
# Summarize results
result <- bins_near_tss %>%
group_by(distanceBin) %>%
summarise(mean_peak_counts = mean(peak_counts, na.rm = TRUE), .groups = \drop\) %>%
mutate(gene = TSS_group[i]$ensembl)
return(result)
})
# Calculate mean and SD for the group
mean_data <- all_results %>%
group_by(distanceBin) %>%
summarise(mean_peak_counts = mean(mean_peak_counts, na.rm = TRUE))
sd_data <- all_results %>%
group_by(distanceBin) %>%
summarise(sd_peak_counts = sd(mean_peak_counts, na.rm = TRUE))
# Join and add group name
summary_data <- left_join(mean_data, sd_data, by = \distanceBin\) %>%
mutate(group = group_name)
return(summary_data)
}
##########################################################
## Import peak
refDir <- here(\../..\, \reference\)
#peak.H3K27ac <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
peak.H3K27ac <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
#peak.H3K27ac <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
## Import 10kb bin
temp <- fread(here(refDir, \mm10.bin.10kb.bed\))
colnames(temp) <- c(\chr\, \start\, \end\)
temp <- temp %>% dplyr::mutate(start = start+1)
bins.10kb <- makeGRangesFromDataFrame(temp)
## Count overlap
counts <- countOverlaps(bins.10kb, peak.H3K27ac)
mcols(bins.10kb)$peak_counts <- counts
## Getting TSS
flankSize <- 1e6
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\)
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
#TSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)
#TSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)
##########################################################
# List of groups to process
groups <- c(\gene.group1\, \gene.group2\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \gene.group1\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \gene.group2\)
# Plot the results
p <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 0.5) +
labs(
x = \Distance bin (10kb)\,
y = \H3K4me3 peak count\
) +
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS),
legend.position = c(0.8, 0.8) # Moves legend inside the plot (x, y relative coordinates)
) +
guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))+
scale_color_manual(values = rev(c(\#777777\, \#F28E2C\)))
##########################################################
fileName <- paste0(\binPeakDensity_RAD21_binaryGroup\)
width <- 32*mmToInch
height <- 35*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### TSS
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n\n####### DOing this for P-S groups\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n# List of groups to process\ngroups <- c(\\psOver0\\, \\psOver1\\, \\psOver2\\, \\psOver3\\, \\psOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver4\\)\n\n# Plot the results\np <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 0.5) +\n  labs(\n    x = \\Distance bin (10kb)\\,\n    y = \\H3K4me3 peak count\\\n  ) +\n  theme_classic() +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    legend.position = c(0.8, 0.8)  # Moves legend inside the plot (x, y relative coordinates)\n  ) +\n  guides(\n    fill = guide_legend(\n      keywidth = 0.2,  # Adjust the width of the legend keys\n      keyheight = 0.2  # Adjust the height of the legend keys\n    ))+\n  scale_color_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))\n\nfileName <- paste0(\\binPeakDensity_RAD21_psGroup11\\)\nwidth <- 32*mmToInch\nheight <- 35*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n### PE\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n# List of groups to process\ngroups <- c(\\peOver0\\, \\peOver1\\, \\peOver2\\, \\peOver3\\, \\peOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver4\\)\n\n# Plot the results\nggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 1) + xlim(0, 10)+\n  labs(\n    title = \\Mean Peak Counts\\,\n    x = \\Distance Bin (10 kb)\\,\n    y = \\Mean Peak Counts\\\n  ) +\n  theme_minimal() +\n  theme(\n    plot.title = element_text(hjust = 0.5, size = 14, face = \\bold\\),\n    axis.text = element_text(size = 12),\n    axis.title = element_text(size = 13)\n  )\n\n###############\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n# List of groups to process\ngroups <- c(\\ppOver0\\, \\ppOver1\\, \\ppOver2\\, \\ppOver3\\, \\ppOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver4\\)\n\n# Plot the results\nggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 1) +\n  labs(\n    title = \\Mean Peak Counts\\,\n    x = \\Distance Bin (10 kb)\\,\n    y = \\Mean Peak Counts\\\n  ) +\n  theme_minimal() +\n  theme(\n    plot.title = element_text(hjust = 0.5, size = 14, face = \\bold\\),\n    axis.text = element_text(size = 12),\n    axis.title = element_text(size = 13)\n  )\n```\n\n<!-- rnb-source-end -->\n"} -->
####### DOing this for P-S groups
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
# List of groups to process
groups <- c(\psOver0\, \psOver1\, \psOver2\, \psOver3\, \psOver4\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver0\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver1\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver2\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver3\)%>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver4\)
# Plot the results
p <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 0.5) +
labs(
x = \Distance bin (10kb)\,
y = \H3K4me3 peak count\
) +
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS),
legend.position = c(0.8, 0.8) # Moves legend inside the plot (x, y relative coordinates)
) +
guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))+
scale_color_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))
fileName <- paste0(\binPeakDensity_RAD21_psGroup11\)
width <- 32*mmToInch
height <- 35*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### PE
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
# List of groups to process
groups <- c(\peOver0\, \peOver1\, \peOver2\, \peOver3\, \peOver4\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver0\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver1\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver2\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver3\)%>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver4\)
# Plot the results
ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 1) + xlim(0, 10)+
labs(
title = \Mean Peak Counts\,
x = \Distance Bin (10 kb)\,
y = \Mean Peak Counts\
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 14, face = \bold\),
axis.text = element_text(size = 12),
axis.title = element_text(size = 13)
)
###############
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
# List of groups to process
groups <- c(\ppOver0\, \ppOver1\, \ppOver2\, \ppOver3\, \ppOver4\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver0\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver1\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver2\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver3\)%>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver4\)
# Plot the results
ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 1) +
labs(
title = \Mean Peak Counts\,
x = \Distance Bin (10 kb)\,
y = \Mean Peak Counts\
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 14, face = \bold\),
axis.text = element_text(size = 12),
axis.title = element_text(size = 13)
)
<!-- rnb-output-end -->
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 -->\n\n```r\n```r\n\n####### DOing this for P-S groups\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n# List of groups to process\ngroups <- c(\\psOver0\\, \\psOver1\\, \\psOver2\\, \\psOver3\\, \\psOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver4\\)\n\n# Plot the results\np <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 0.5) +\n  labs(\n    x = \\Distance bin (10kb)\\,\n    y = \\H3K4me3 peak count\\\n  ) +\n  theme_classic() +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    legend.position = c(0.8, 0.8)  # Moves legend inside the plot (x, y relative coordinates)\n  ) +\n  guides(\n    fill = guide_legend(\n      keywidth = 0.2,  # Adjust the width of the legend keys\n      keyheight = 0.2  # Adjust the height of the legend keys\n    ))+\n  scale_color_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))\n\nfileName <- paste0(\\binPeakDensity_RAD21_psGroup11\\)\nwidth <- 32*mmToInch\nheight <- 35*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n### PE\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n# List of groups to process\ngroups <- c(\\peOver0\\, \\peOver1\\, \\peOver2\\, \\peOver3\\, \\peOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver4\\)\n\n# Plot the results\nggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 1) + xlim(0, 10)+\n  labs(\n    title = \\Mean Peak Counts\\,\n    x = \\Distance Bin (10 kb)\\,\n    y = \\Mean Peak Counts\\\n  ) +\n  theme_minimal() +\n  theme(\n    plot.title = element_text(hjust = 0.5, size = 14, face = \\bold\\),\n    axis.text = element_text(size = 12),\n    axis.title = element_text(size = 13)\n  )\n\n###############\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n# List of groups to process\ngroups <- c(\\ppOver0\\, \\ppOver1\\, \\ppOver2\\, \\ppOver3\\, \\ppOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver4\\)\n\n# Plot the results\nggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 1) +\n  labs(\n    title = \\Mean Peak Counts\\,\n    x = \\Distance Bin (10 kb)\\,\n    y = \\Mean Peak Counts\\\n  ) +\n  theme_minimal() +\n  theme(\n    plot.title = element_text(hjust = 0.5, size = 14, face = \\bold\\),\n    axis.text = element_text(size = 12),\n    axis.title = element_text(size = 13)\n  )\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n\n####### DOing this for P-S groups\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n# List of groups to process\ngroups <- c(\\psOver0\\, \\psOver1\\, \\psOver2\\, \\psOver3\\, \\psOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\psOver4\\)\n\n# Plot the results\np <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 0.5) +\n  labs(\n    x = \\Distance bin (10kb)\\,\n    y = \\H3K4me3 peak count\\\n  ) +\n  theme_classic() +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS),\n    legend.position = c(0.8, 0.8)  # Moves legend inside the plot (x, y relative coordinates)\n  ) +\n  guides(\n    fill = guide_legend(\n      keywidth = 0.2,  # Adjust the width of the legend keys\n      keyheight = 0.2  # Adjust the height of the legend keys\n    ))+\n  scale_color_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\))\n\nfileName <- paste0(\\binPeakDensity_RAD21_psGroup11\\)\nwidth <- 32*mmToInch\nheight <- 35*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n### PE\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n# List of groups to process\ngroups <- c(\\peOver0\\, \\peOver1\\, \\peOver2\\, \\peOver3\\, \\peOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\peOver4\\)\n\n# Plot the results\nggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 1) + xlim(0, 10)+\n  labs(\n    title = \\Mean Peak Counts\\,\n    x = \\Distance Bin (10 kb)\\,\n    y = \\Mean Peak Counts\\\n  ) +\n  theme_minimal() +\n  theme(\n    plot.title = element_text(hjust = 0.5, size = 14, face = \\bold\\),\n    axis.text = element_text(size = 12),\n    axis.title = element_text(size = 13)\n  )\n\n###############\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n# List of groups to process\ngroups <- c(\\ppOver0\\, \\ppOver1\\, \\ppOver2\\, \\ppOver3\\, \\ppOver4\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver0\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver1\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver2\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver3\\)%>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\ppOver4\\)\n\n# Plot the results\nggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 1) +\n  labs(\n    title = \\Mean Peak Counts\\,\n    x = \\Distance Bin (10 kb)\\,\n    y = \\Mean Peak Counts\\\n  ) +\n  theme_minimal() +\n  theme(\n    plot.title = element_text(hjust = 0.5, size = 14, face = \\bold\\),\n    axis.text = element_text(size = 12),\n    axis.title = element_text(size = 13)\n  )\n```\n```"} -->
```r
```r
####### DOing this for P-S groups
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
# List of groups to process
groups <- c(\psOver0\, \psOver1\, \psOver2\, \psOver3\, \psOver4\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver0\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver1\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver2\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver3\)%>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \psOver4\)
# Plot the results
p <- ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 0.5) +
labs(
x = \Distance bin (10kb)\,
y = \H3K4me3 peak count\
) +
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS),
legend.position = c(0.8, 0.8) # Moves legend inside the plot (x, y relative coordinates)
) +
guides(
fill = guide_legend(
keywidth = 0.2, # Adjust the width of the legend keys
keyheight = 0.2 # Adjust the height of the legend keys
))+
scale_color_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))
fileName <- paste0(\binPeakDensity_RAD21_psGroup11\)
width <- 32*mmToInch
height <- 35*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### PE
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
# List of groups to process
groups <- c(\peOver0\, \peOver1\, \peOver2\, \peOver3\, \peOver4\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver0\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver1\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver2\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver3\)%>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \peOver4\)
# Plot the results
ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 1) + xlim(0, 10)+
labs(
title = \Mean Peak Counts\,
x = \Distance Bin (10 kb)\,
y = \Mean Peak Counts\
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 14, face = \bold\),
axis.text = element_text(size = 12),
axis.title = element_text(size = 13)
)
###############
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
# List of groups to process
groups <- c(\ppOver0\, \ppOver1\, \ppOver2\, \ppOver3\, \ppOver4\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver0\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver1\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver2\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver3\)%>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \ppOver4\)
# Plot the results
ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 1) +
labs(
title = \Mean Peak Counts\,
x = \Distance Bin (10 kb)\,
y = \Mean Peak Counts\
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 14, face = \bold\),
axis.text = element_text(size = 12),
axis.title = element_text(size = 13)
)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
###[2.30] H3K27ac peak number per 10kb
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\npeak.Whyte.SE <- importPeak(here(refDir, \\superEnhancer_Whyte_ESC_mm10.bed\\))\npeak.Dylan.SE <- importPeak(here(refDir, \\superEnhancer_Dylan_ESC.bed\\))\n\n\n\nflankSize <- 0.5e6\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6, V5)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\, \\ensembl\\, \\gene\\)\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nTSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)\nTSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)\n\n\nlength(unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Whyte.SE))))/length(gene.group1)*100\nlength(unique(queryHits(findOverlaps(TSS1mb.group2.gr, peak.Whyte.SE))))/length(gene.group2)*100\n\nlength(unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE))))/length(gene.group1)*100\nlength(unique(queryHits(findOverlaps(TSS1mb.group2.gr, peak.Dylan.SE))))/length(gene.group2)*100\n\n#View(as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Whyte.SE)))]))\n#View(as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE)))]))\n\ngene.group1.SE <- (as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE)))]))$ensembl\ngene.group1.noSE <- gene.group1[!(gene.group1 %in% gene.group1.SE)]\n\noverlaps <- findOverlaps(TSS1mb.group2.gr, peak.Dylan.SE)\nunique_hits <- unique(queryHits(overlaps))\ngene.group2.SE <- as_tibble(TSS1mb.group2.gr[unique_hits])$ensembl\ngene.group2.noSE <- as_tibble(TSS1mb.group2.gr[-unique_hits])$ensembl\n\n\n##########################################################\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - 1e6,\n                TSSend = TSS + 1e6) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6, V5)\ncolnames(gene.tb) <- c(\\chr\\, \\start\\, \\end\\, \\ensembl\\, \\gene\\)\n\n##########################################################\n# List of groups to process\ngroups <- c(\\gene.group1.SE\\, \\gene.group1.noSE\\)\n\n# Process each group and combine results\nsummary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))\n\n# Add the additional rows for distanceBin = 0\nsummary_data <- summary_data %>%\n  mutate(distanceBin = distanceBin + 1) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\gene.group1.SE\\) %>%\n  add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \\gene.group1.noSE\\)\n\n# Plot the results\nggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) + \n  geom_line(size = 1) +\n  labs(\n    title = paste0(\\Mean Peak Counts\n"} -->
peak.Whyte.SE <- importPeak(here(refDir, \superEnhancer_Whyte_ESC_mm10.bed\))
peak.Dylan.SE <- importPeak(here(refDir, \superEnhancer_Dylan_ESC.bed\))
flankSize <- 0.5e6
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\, \gene\)
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
TSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)
TSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)
length(unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Whyte.SE))))/length(gene.group1)*100
length(unique(queryHits(findOverlaps(TSS1mb.group2.gr, peak.Whyte.SE))))/length(gene.group2)*100
length(unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE))))/length(gene.group1)*100
length(unique(queryHits(findOverlaps(TSS1mb.group2.gr, peak.Dylan.SE))))/length(gene.group2)*100
#View(as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Whyte.SE)))]))
#View(as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE)))]))
gene.group1.SE <- (as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE)))]))$ensembl
gene.group1.noSE <- gene.group1[!(gene.group1 %in% gene.group1.SE)]
overlaps <- findOverlaps(TSS1mb.group2.gr, peak.Dylan.SE)
unique_hits <- unique(queryHits(overlaps))
gene.group2.SE <- as_tibble(TSS1mb.group2.gr[unique_hits])$ensembl
gene.group2.noSE <- as_tibble(TSS1mb.group2.gr[-unique_hits])$ensembl
##########################################################
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - 1e6,
TSSend = TSS + 1e6) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\, \gene\)
##########################################################
# List of groups to process
groups <- c(\gene.group1.SE\, \gene.group1.noSE\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \gene.group1.SE\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \gene.group1.noSE\)
# Plot the results
ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 1) +
labs(
title = paste0(\Mean Peak Counts```r
peak.Whyte.SE <- importPeak(here(refDir, \superEnhancer_Whyte_ESC_mm10.bed\))
peak.Dylan.SE <- importPeak(here(refDir, \superEnhancer_Dylan_ESC.bed\))
flankSize <- 0.5e6
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\, \gene\)
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
TSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)
TSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)
length(unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Whyte.SE))))/length(gene.group1)*100
length(unique(queryHits(findOverlaps(TSS1mb.group2.gr, peak.Whyte.SE))))/length(gene.group2)*100
length(unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE))))/length(gene.group1)*100
length(unique(queryHits(findOverlaps(TSS1mb.group2.gr, peak.Dylan.SE))))/length(gene.group2)*100
#View(as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Whyte.SE)))]))
#View(as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE)))]))
gene.group1.SE <- (as_tibble(TSS1mb.group1.gr[unique(queryHits(findOverlaps(TSS1mb.group1.gr, peak.Dylan.SE)))]))$ensembl
gene.group1.noSE <- gene.group1[!(gene.group1 %in% gene.group1.SE)]
overlaps <- findOverlaps(TSS1mb.group2.gr, peak.Dylan.SE)
unique_hits <- unique(queryHits(overlaps))
gene.group2.SE <- as_tibble(TSS1mb.group2.gr[unique_hits])$ensembl
gene.group2.noSE <- as_tibble(TSS1mb.group2.gr[-unique_hits])$ensembl
##########################################################
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - 1e6,
TSSend = TSS + 1e6) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\, \gene\)
##########################################################
# List of groups to process
groups <- c(\gene.group1.SE\, \gene.group1.noSE\)
# Process each group and combine results
summary_data <- map_dfr(groups, ~ process_group(.x, gene.tb, bins.10kb))
# Add the additional rows for distanceBin = 0
summary_data <- summary_data %>%
mutate(distanceBin = distanceBin + 1) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \gene.group1.SE\) %>%
add_row(distanceBin = 0, mean_peak_counts = 0, sd_peak_counts = 0, group = \gene.group1.noSE\)
# Plot the results
ggplot(summary_data, aes(x = distanceBin, y = mean_peak_counts, color = group)) +
geom_line(size = 1) +
labs(
title = paste0(\Mean Peak Counts
<!-- rnb-source-begin {"data":"```r\nname <- \"chromo_cons_annoHierarchy\"\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \"_p-n_ensemblList.tsv\")),\n                                      diffCutoff = diffCutoff)%>% \n  dplyr::mutate(distance = start2 - start1)\n\n\ngeneAnnoData <- geneAnnoData %>% unnest(gene)\n\n\ngene.tb <- fread(here(refDir, \"mm10_GRCm38.p6_gene_sorted.bed\")) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \"+\", V2, V3)) %>%\n  dplyr::select(V6, TSS)\ncolnames(gene.tb) <- c(\"ensembl\", \"TSS\")\n\n\ngeneAnnoData  <- geneAnnoData %>% dplyr::left_join(gene.tb, by = c(\"gene\" = \"ensembl\"))\n\ndata <- geneAnnoData %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 29, 31, 35)) %>%\n  rowwise() %>%\n  dplyr::mutate(distToAnchor1 = (start1 + end1)/2 - TSS,\n                distToAnchor2 = (start2 + end2)/2 - TSS,\n                distToAnchor = if_else(abs(distToAnchor1) > abs(distToAnchor2), distToAnchor1, distToAnchor2),\n                direction = if_else(distToAnchor > 0, \"right\", \"left\"))\n\ndata.directionality <- data %>% group_by(gene) %>%\n  summarise(count = n(),\n            n_right = sum(direction == \"right\"),\n            n_left = sum(direction == \"left\")) %>%\n  dplyr::filter(count > 1) %>%\n  dplyr::mutate(n = n_right + n_left,\n                directionality = abs((n_right - n_left)/n))\n\n\n#ggplot(data.directionality, aes(x = directionality)) + geom_histogram() + theme_classic()\n\n\ngene.group1 <- fread(here(refDir, \"geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\"))$gene\ngene.group2 <- fread(here(refDir, \"geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\"))$gene\n\ntemp <- data.directionality %>% dplyr::filter(gene %in% c(gene.group1, gene.group2)) %>%\n  dplyr::mutate(group = if_else(gene %in% gene.group1, \"group1\", \"group2\"))\n\n\nggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) + \n  geom_hline(yintercept = 0) + theme_classic() + ggtitle(\"P-S\") +\n  stat_summary(aes(group = group), fun = mean, geom = \"point\", shape = 21, size = 2, fill = \"red\", color = \"black\")\n\n\n#########################PS\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$directionality\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$directionality\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n\nresultDir <- here(\"../../result\")\ntemp2 <- readRDS(here(resultDir, \"gene_loop_link.rds\"))\n#####################\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\ntemp <- data.directionality %>% dplyr::filter(gene %in% c(psOver0, psOver1, psOver2, psOver3, psOver4)) %>%\n  dplyr::mutate(group = case_when(gene %in% psOver0 ~ \"psOver0\",\n                                  gene %in% psOver1 ~ \"psOver1\",\n                                  gene %in% psOver2 ~ \"psOver2\",\n                                  gene %in% psOver3 ~ \"psOver3\",\n                                  gene %in% psOver4 ~ \"psOver4\"))\n\np34 <- round(getPvalWilcox(temp, \"psOver3\", \"psOver4\"), 5)\np23 <- round(getPvalWilcox(temp, \"psOver2\", \"psOver3\"), 5)\np12 <- round(getPvalWilcox(temp, \"psOver1\", \"psOver2\"), 5)\np01 <- round(getPvalWilcox(temp, \"psOver0\", \"psOver1\"), 5)\np04 <- round(getPvalWilcox(temp, \"psOver0\", \"psOver4\"), 5)\n\n\n\nggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) + \n  geom_hline(yintercept = 0) + theme_classic() + ggtitle(\"P-N\") +\n  stat_summary(aes(group = group), fun = mean, geom = \"point\", shape = 21, size = 2, fill = \"red\", color = \"black\") +\n  annotate(\"text\", x = 1, y = 0.5, label = paste0(\"p34: \", convPvalue(p34), \"\\n\",\n                                                \"p23: \", convPvalue(p23), \"\\n\",\n                                                \"p12: \", convPvalue(p12), \"\\n\",\n                                                \"p01: \", convPvalue(p01), \"\\n\",\n                                                \"p04: \", convPvalue(p04), \"\\n\"),\n           color = \"black\", hjust = 0, size = 3)\n\n\n#################### pe\n\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\ntemp <- data.directionality %>% dplyr::filter(gene %in% c(peOver0, peOver1, peOver2, peOver3, peOver4)) %>%\n  dplyr::mutate(group = case_when(gene %in% peOver0 ~ \"peOver0\",\n                                  gene %in% peOver1 ~ \"peOver1\",\n                                  gene %in% peOver2 ~ \"peOver2\",\n                                  gene %in% peOver3 ~ \"peOver3\",\n                                  gene %in% peOver4 ~ \"peOver4\"))\n\np34 <- round(getPvalWilcox(temp, \"peOver3\", \"peOver4\"), 5)\np23 <- round(getPvalWilcox(temp, \"peOver2\", \"peOver3\"), 5)\np12 <- round(getPvalWilcox(temp, \"peOver1\", \"peOver2\"), 5)\np01 <- round(getPvalWilcox(temp, \"peOver0\", \"peOver1\"), 5)\np04 <- round(getPvalWilcox(temp, \"peOver0\", \"peOver4\"), 5)\n\n\n\nggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) + \n  geom_hline(yintercept = 0) + theme_classic() + ggtitle(\"P-N\") +\n  stat_summary(aes(group = group), fun = mean, geom = \"point\", shape = 21, size = 2, fill = \"red\", color = \"black\") +\n  annotate(\"text\", x = 1, y = 0.5, label = paste0(\"p34: \", convPvalue(p34), \"\\n\",\n                                                \"p23: \", convPvalue(p23), \"\\n\",\n                                                \"p12: \", convPvalue(p12), \"\\n\",\n                                                \"p01: \", convPvalue(p01), \"\\n\",\n                                                \"p04: \", convPvalue(p04), \"\\n\"),\n           color = \"black\", hjust = 0, size = 3)\n\n\n#################### pp\n\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\ntemp <- data.directionality %>% dplyr::filter(gene %in% c(ppOver0, ppOver1, ppOver2, ppOver3, ppOver4)) %>%\n  dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \"ppOver0\",\n                                  gene %in% ppOver1 ~ \"ppOver1\",\n                                  gene %in% ppOver2 ~ \"ppOver2\",\n                                  gene %in% ppOver3 ~ \"ppOver3\",\n                                  gene %in% ppOver4 ~ \"ppOver4\"))\n\np34 <- round(getPvalWilcox(temp, \"ppOver3\", \"ppOver4\"), 5)\np23 <- round(getPvalWilcox(temp, \"ppOver2\", \"ppOver3\"), 5)\np12 <- round(getPvalWilcox(temp, \"ppOver1\", \"ppOver2\"), 5)\np01 <- round(getPvalWilcox(temp, \"ppOver0\", \"ppOver1\"), 5)\np04 <- round(getPvalWilcox(temp, \"ppOver0\", \"ppOver4\"), 5)\n\n\n\nggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) + \n  geom_hline(yintercept = 0) + theme_classic() + ggtitle(\"P-N\") +\n  stat_summary(aes(group = group), fun = mean, geom = \"point\", shape = 21, size = 2, fill = \"red\", color = \"black\") +\n  annotate(\"text\", x = 1, y = 0.5, label = paste0(\"p34: \", convPvalue(p34), \"\\n\",\n                                                \"p23: \", convPvalue(p23), \"\\n\",\n                                                \"p12: \", convPvalue(p12), \"\\n\",\n                                                \"p01: \", convPvalue(p01), \"\\n\",\n                                                \"p04: \", convPvalue(p04), \"\\n\"),\n           color = \"black\", hjust = 0, size = 3)\n\n\n```"} -->
```r
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff)%>%
dplyr::mutate(distance = start2 - start1)
geneAnnoData <- geneAnnoData %>% unnest(gene)
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, TSS)
colnames(gene.tb) <- c(\ensembl\, \TSS\)
geneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.tb, by = c(\gene\ = \ensembl\))
data <- geneAnnoData %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 29, 31, 35)) %>%
rowwise() %>%
dplyr::mutate(distToAnchor1 = (start1 + end1)/2 - TSS,
distToAnchor2 = (start2 + end2)/2 - TSS,
distToAnchor = if_else(abs(distToAnchor1) > abs(distToAnchor2), distToAnchor1, distToAnchor2),
direction = if_else(distToAnchor > 0, \right\, \left\))
data.directionality <- data %>% group_by(gene) %>%
summarise(count = n(),
n_right = sum(direction == \right\),
n_left = sum(direction == \left\)) %>%
dplyr::filter(count > 1) %>%
dplyr::mutate(n = n_right + n_left,
directionality = abs((n_right - n_left)/n))
#ggplot(data.directionality, aes(x = directionality)) + geom_histogram() + theme_classic()
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
temp <- data.directionality %>% dplyr::filter(gene %in% c(gene.group1, gene.group2)) %>%
dplyr::mutate(group = if_else(gene %in% gene.group1, \group1\, \group2\))
ggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) +
geom_hline(yintercept = 0) + theme_classic() + ggtitle(\P-S\) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
#########################PS
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$directionality
distance2 <- (data %>% dplyr::filter(group ==group2) )$directionality
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
#####################
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
temp <- data.directionality %>% dplyr::filter(gene %in% c(psOver0, psOver1, psOver2, psOver3, psOver4)) %>%
dplyr::mutate(group = case_when(gene %in% psOver0 ~ \psOver0\,
gene %in% psOver1 ~ \psOver1\,
gene %in% psOver2 ~ \psOver2\,
gene %in% psOver3 ~ \psOver3\,
gene %in% psOver4 ~ \psOver4\))
p34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
p23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
p12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
p01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
p04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
ggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) +
geom_hline(yintercept = 0) + theme_classic() + ggtitle(\P-N\) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 0.5, label = paste0(\p34: \, convPvalue(p34), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p01: \, convPvalue(p01), \\n\,
\p04: \, convPvalue(p04), \\n\),
color = \black\, hjust = 0, size = 3)
#################### pe
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
temp <- data.directionality %>% dplyr::filter(gene %in% c(peOver0, peOver1, peOver2, peOver3, peOver4)) %>%
dplyr::mutate(group = case_when(gene %in% peOver0 ~ \peOver0\,
gene %in% peOver1 ~ \peOver1\,
gene %in% peOver2 ~ \peOver2\,
gene %in% peOver3 ~ \peOver3\,
gene %in% peOver4 ~ \peOver4\))
p34 <- round(getPvalWilcox(temp, \peOver3\, \peOver4\), 5)
p23 <- round(getPvalWilcox(temp, \peOver2\, \peOver3\), 5)
p12 <- round(getPvalWilcox(temp, \peOver1\, \peOver2\), 5)
p01 <- round(getPvalWilcox(temp, \peOver0\, \peOver1\), 5)
p04 <- round(getPvalWilcox(temp, \peOver0\, \peOver4\), 5)
ggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) +
geom_hline(yintercept = 0) + theme_classic() + ggtitle(\P-N\) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 0.5, label = paste0(\p34: \, convPvalue(p34), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p01: \, convPvalue(p01), \\n\,
\p04: \, convPvalue(p04), \\n\),
color = \black\, hjust = 0, size = 3)
#################### pp
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
temp <- data.directionality %>% dplyr::filter(gene %in% c(ppOver0, ppOver1, ppOver2, ppOver3, ppOver4)) %>%
dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \ppOver0\,
gene %in% ppOver1 ~ \ppOver1\,
gene %in% ppOver2 ~ \ppOver2\,
gene %in% ppOver3 ~ \ppOver3\,
gene %in% ppOver4 ~ \ppOver4\))
p34 <- round(getPvalWilcox(temp, \ppOver3\, \ppOver4\), 5)
p23 <- round(getPvalWilcox(temp, \ppOver2\, \ppOver3\), 5)
p12 <- round(getPvalWilcox(temp, \ppOver1\, \ppOver2\), 5)
p01 <- round(getPvalWilcox(temp, \ppOver0\, \ppOver1\), 5)
p04 <- round(getPvalWilcox(temp, \ppOver0\, \ppOver4\), 5)
ggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) +
geom_hline(yintercept = 0) + theme_classic() + ggtitle(\P-N\) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 0.5, label = paste0(\p34: \, convPvalue(p34), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p01: \, convPvalue(p01), \\n\,
\p04: \, convPvalue(p04), \\n\),
color = \black\, hjust = 0, size = 3)
```
<!-- rnb-source-end -->```r
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff)%>%
dplyr::mutate(distance = start2 - start1)
geneAnnoData <- geneAnnoData %>% unnest(gene)
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, TSS)
colnames(gene.tb) <- c(\ensembl\, \TSS\)
geneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.tb, by = c(\gene\ = \ensembl\))
data <- geneAnnoData %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 29, 31, 35)) %>%
rowwise() %>%
dplyr::mutate(distToAnchor1 = (start1 + end1)/2 - TSS,
distToAnchor2 = (start2 + end2)/2 - TSS,
distToAnchor = if_else(abs(distToAnchor1) > abs(distToAnchor2), distToAnchor1, distToAnchor2),
direction = if_else(distToAnchor > 0, \right\, \left\))
data.directionality <- data %>% group_by(gene) %>%
summarise(count = n(),
n_right = sum(direction == \right\),
n_left = sum(direction == \left\)) %>%
dplyr::filter(count > 1) %>%
dplyr::mutate(n = n_right + n_left,
directionality = abs((n_right - n_left)/n))
#ggplot(data.directionality, aes(x = directionality)) + geom_histogram() + theme_classic()
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
temp <- data.directionality %>% dplyr::filter(gene %in% c(gene.group1, gene.group2)) %>%
dplyr::mutate(group = if_else(gene %in% gene.group1, \group1\, \group2\))
ggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) +
geom_hline(yintercept = 0) + theme_classic() + ggtitle(\P-S\) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
#########################PS
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$directionality
distance2 <- (data %>% dplyr::filter(group ==group2) )$directionality
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
#####################
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
temp <- data.directionality %>% dplyr::filter(gene %in% c(psOver0, psOver1, psOver2, psOver3, psOver4)) %>%
dplyr::mutate(group = case_when(gene %in% psOver0 ~ \psOver0\,
gene %in% psOver1 ~ \psOver1\,
gene %in% psOver2 ~ \psOver2\,
gene %in% psOver3 ~ \psOver3\,
gene %in% psOver4 ~ \psOver4\))
p34 <- round(getPvalWilcox(temp, \psOver3\, \psOver4\), 5)
p23 <- round(getPvalWilcox(temp, \psOver2\, \psOver3\), 5)
p12 <- round(getPvalWilcox(temp, \psOver1\, \psOver2\), 5)
p01 <- round(getPvalWilcox(temp, \psOver0\, \psOver1\), 5)
p04 <- round(getPvalWilcox(temp, \psOver0\, \psOver4\), 5)
ggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) +
geom_hline(yintercept = 0) + theme_classic() + ggtitle(\P-N\) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 0.5, label = paste0(\p34: \, convPvalue(p34), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p01: \, convPvalue(p01), \\n\,
\p04: \, convPvalue(p04), \\n\),
color = \black\, hjust = 0, size = 3)
#################### pe
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
temp <- data.directionality %>% dplyr::filter(gene %in% c(peOver0, peOver1, peOver2, peOver3, peOver4)) %>%
dplyr::mutate(group = case_when(gene %in% peOver0 ~ \peOver0\,
gene %in% peOver1 ~ \peOver1\,
gene %in% peOver2 ~ \peOver2\,
gene %in% peOver3 ~ \peOver3\,
gene %in% peOver4 ~ \peOver4\))
p34 <- round(getPvalWilcox(temp, \peOver3\, \peOver4\), 5)
p23 <- round(getPvalWilcox(temp, \peOver2\, \peOver3\), 5)
p12 <- round(getPvalWilcox(temp, \peOver1\, \peOver2\), 5)
p01 <- round(getPvalWilcox(temp, \peOver0\, \peOver1\), 5)
p04 <- round(getPvalWilcox(temp, \peOver0\, \peOver4\), 5)
ggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) +
geom_hline(yintercept = 0) + theme_classic() + ggtitle(\P-N\) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 0.5, label = paste0(\p34: \, convPvalue(p34), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p01: \, convPvalue(p01), \\n\,
\p04: \, convPvalue(p04), \\n\),
color = \black\, hjust = 0, size = 3)
#################### pp
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
temp <- data.directionality %>% dplyr::filter(gene %in% c(ppOver0, ppOver1, ppOver2, ppOver3, ppOver4)) %>%
dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \ppOver0\,
gene %in% ppOver1 ~ \ppOver1\,
gene %in% ppOver2 ~ \ppOver2\,
gene %in% ppOver3 ~ \ppOver3\,
gene %in% ppOver4 ~ \ppOver4\))
p34 <- round(getPvalWilcox(temp, \ppOver3\, \ppOver4\), 5)
p23 <- round(getPvalWilcox(temp, \ppOver2\, \ppOver3\), 5)
p12 <- round(getPvalWilcox(temp, \ppOver1\, \ppOver2\), 5)
p01 <- round(getPvalWilcox(temp, \ppOver0\, \ppOver1\), 5)
p04 <- round(getPvalWilcox(temp, \ppOver0\, \ppOver4\), 5)
ggplot(temp, aes(x = group, y = directionality)) + geom_violin() + geom_boxplot(width = 0.1) +
geom_hline(yintercept = 0) + theme_classic() + ggtitle(\P-N\) +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
annotate(\text\, x = 1, y = 0.5, label = paste0(\p34: \, convPvalue(p34), \\n\,
\p23: \, convPvalue(p23), \\n\,
\p12: \, convPvalue(p12), \\n\,
\p01: \, convPvalue(p01), \\n\,
\p04: \, convPvalue(p04), \\n\),
color = \black\, hjust = 0, size = 3)
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.31] Testing SEs?
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff)%>% \n  dplyr::mutate(distance = start2 - start1)\n\ngeneAnnoData <- geneAnnoData %>% unnest(gene)\n\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V6, TSS)\ncolnames(gene.tb) <- c(\\ensembl\\, \\TSS\\)\n\n\ngeneAnnoData  <- geneAnnoData %>% dplyr::left_join(gene.tb, by = c(\\gene\\ = \\ensembl\\))\n\ndata <- geneAnnoData %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 29, 31, 35)) %>%\n  rowwise() %>%\n  dplyr::mutate(distToAnchor1 = (start1 + end1)/2 - TSS,\n                distToAnchor2 = (start2 + end2)/2 - TSS,\n                distToAnchor = if_else(abs(distToAnchor1) > abs(distToAnchor2), distToAnchor1, distToAnchor2),\n                direction = if_else(distToAnchor > 0, \\right\\, \\left\\))\n\n\nresult <- data %>%\n  group_by(gene, direction) %>%\n  mutate(distToAnchor = abs(distToAnchor)) %>%\n  mutate(max_dist_P_S = ifelse(any(Anno2 == \\P-S\\), \n                               max(distToAnchor[Anno2 == \\P-S\\], na.rm = TRUE), \n                               NA)) %>%\n  mutate(is_smaller = ifelse(is.na(max_dist_P_S), NA, \n                             ifelse(distToAnchor == max_dist_P_S, NA, distToAnchor < max_dist_P_S))) %>%\n  ungroup()\n\n\n\nresult2 <- result %>% dplyr::filter(!is.na(is_smaller))\n\nresult3 <- result2 %>% group_by(gene, direction) %>%\n  summarize(count_true = sum(is_smaller, na.rm = TRUE),\n            count_false = sum(is_smaller == FALSE, na.rm = TRUE)) %>%\n  dplyr::mutate(count = count_true + count_false,\n                perc = count_true/count*100) %>%\n  dplyr::select(gene, direction, perc)\n\nresult3 <- result3 %>% group_by(gene) %>% summarize(perc = mean(perc))\n\n####### DOing this for P-S groups\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% psOver0 ~ \\psOver0\\,\n                                            gene %in% psOver1 ~ \\psOver1\\,\n                                            gene %in% psOver2 ~ \\psOver2\\,\n                                            gene %in% psOver3 ~ \\psOver3\\,\n                                            gene %in% psOver4 ~ \\psOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n####### DOing this for P-E groups\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% peOver0 ~ \\peOver0\\,\n                                            gene %in% peOver1 ~ \\peOver1\\,\n                                            gene %in% peOver2 ~ \\peOver2\\,\n                                            gene %in% peOver3 ~ \\peOver3\\,\n                                            gene %in% peOver4 ~ \\peOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n####### DOing this for P-P groups\n\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \\ppOver0\\,\n                                            gene %in% ppOver1 ~ \\ppOver1\\,\n                                            gene %in% ppOver2 ~ \\ppOver2\\,\n                                            gene %in% ppOver3 ~ \\ppOver3\\,\n                                            gene %in% ppOver4 ~ \\ppOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff)%>%
dplyr::mutate(distance = start2 - start1)
geneAnnoData <- geneAnnoData %>% unnest(gene)
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, TSS)
colnames(gene.tb) <- c(\ensembl\, \TSS\)
geneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.tb, by = c(\gene\ = \ensembl\))
data <- geneAnnoData %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 29, 31, 35)) %>%
rowwise() %>%
dplyr::mutate(distToAnchor1 = (start1 + end1)/2 - TSS,
distToAnchor2 = (start2 + end2)/2 - TSS,
distToAnchor = if_else(abs(distToAnchor1) > abs(distToAnchor2), distToAnchor1, distToAnchor2),
direction = if_else(distToAnchor > 0, \right\, \left\))
result <- data %>%
group_by(gene, direction) %>%
mutate(distToAnchor = abs(distToAnchor)) %>%
mutate(max_dist_P_S = ifelse(any(Anno2 == \P-S\),
max(distToAnchor[Anno2 == \P-S\], na.rm = TRUE),
NA)) %>%
mutate(is_smaller = ifelse(is.na(max_dist_P_S), NA,
ifelse(distToAnchor == max_dist_P_S, NA, distToAnchor < max_dist_P_S))) %>%
ungroup()
result2 <- result %>% dplyr::filter(!is.na(is_smaller))
result3 <- result2 %>% group_by(gene, direction) %>%
summarize(count_true = sum(is_smaller, na.rm = TRUE),
count_false = sum(is_smaller == FALSE, na.rm = TRUE)) %>%
dplyr::mutate(count = count_true + count_false,
perc = count_true/count*100) %>%
dplyr::select(gene, direction, perc)
result3 <- result3 %>% group_by(gene) %>% summarize(perc = mean(perc))
####### DOing this for P-S groups
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% psOver0 ~ \psOver0\,
gene %in% psOver1 ~ \psOver1\,
gene %in% psOver2 ~ \psOver2\,
gene %in% psOver3 ~ \psOver3\,
gene %in% psOver4 ~ \psOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
####### DOing this for P-E groups
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% peOver0 ~ \peOver0\,
gene %in% peOver1 ~ \peOver1\,
gene %in% peOver2 ~ \peOver2\,
gene %in% peOver3 ~ \peOver3\,
gene %in% peOver4 ~ \peOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
####### DOing this for P-P groups
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \ppOver0\,
gene %in% ppOver1 ~ \ppOver1\,
gene %in% ppOver2 ~ \ppOver2\,
gene %in% ppOver3 ~ \ppOver3\,
gene %in% ppOver4 ~ \ppOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
<!-- rnb-output-end -->
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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff)%>% \n  dplyr::mutate(distance = start2 - start1)\n\ngeneAnnoData <- geneAnnoData %>% unnest(gene)\n\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V6, TSS)\ncolnames(gene.tb) <- c(\\ensembl\\, \\TSS\\)\n\n\ngeneAnnoData  <- geneAnnoData %>% dplyr::left_join(gene.tb, by = c(\\gene\\ = \\ensembl\\))\n\ndata <- geneAnnoData %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 29, 31, 35)) %>%\n  rowwise() %>%\n  dplyr::mutate(distToAnchor1 = (start1 + end1)/2 - TSS,\n                distToAnchor2 = (start2 + end2)/2 - TSS,\n                distToAnchor = if_else(abs(distToAnchor1) > abs(distToAnchor2), distToAnchor1, distToAnchor2),\n                direction = if_else(distToAnchor > 0, \\right\\, \\left\\))\n\n\nresult <- data %>%\n  group_by(gene, direction) %>%\n  mutate(distToAnchor = abs(distToAnchor)) %>%\n  mutate(max_dist_P_S = ifelse(any(Anno2 == \\P-S\\), \n                               max(distToAnchor[Anno2 == \\P-S\\], na.rm = TRUE), \n                               NA)) %>%\n  mutate(is_smaller = ifelse(is.na(max_dist_P_S), NA, \n                             ifelse(distToAnchor == max_dist_P_S, NA, distToAnchor < max_dist_P_S))) %>%\n  ungroup()\n\n\n\nresult2 <- result %>% dplyr::filter(!is.na(is_smaller))\n\nresult3 <- result2 %>% group_by(gene, direction) %>%\n  summarize(count_true = sum(is_smaller, na.rm = TRUE),\n            count_false = sum(is_smaller == FALSE, na.rm = TRUE)) %>%\n  dplyr::mutate(count = count_true + count_false,\n                perc = count_true/count*100) %>%\n  dplyr::select(gene, direction, perc)\n\nresult3 <- result3 %>% group_by(gene) %>% summarize(perc = mean(perc))\n\n####### DOing this for P-S groups\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% psOver0 ~ \\psOver0\\,\n                                            gene %in% psOver1 ~ \\psOver1\\,\n                                            gene %in% psOver2 ~ \\psOver2\\,\n                                            gene %in% psOver3 ~ \\psOver3\\,\n                                            gene %in% psOver4 ~ \\psOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n####### DOing this for P-E groups\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% peOver0 ~ \\peOver0\\,\n                                            gene %in% peOver1 ~ \\peOver1\\,\n                                            gene %in% peOver2 ~ \\peOver2\\,\n                                            gene %in% peOver3 ~ \\peOver3\\,\n                                            gene %in% peOver4 ~ \\peOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n####### DOing this for P-P groups\n\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \\ppOver0\\,\n                                            gene %in% ppOver1 ~ \\ppOver1\\,\n                                            gene %in% ppOver2 ~ \\ppOver2\\,\n                                            gene %in% ppOver3 ~ \\ppOver3\\,\n                                            gene %in% ppOver4 ~ \\ppOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff)%>%
dplyr::mutate(distance = start2 - start1)
geneAnnoData <- geneAnnoData %>% unnest(gene)
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, TSS)
colnames(gene.tb) <- c(\ensembl\, \TSS\)
geneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.tb, by = c(\gene\ = \ensembl\))
data <- geneAnnoData %>% dplyr::select(c(1, 2, 3, 4, 5, 6, 29, 31, 35)) %>%
rowwise() %>%
dplyr::mutate(distToAnchor1 = (start1 + end1)/2 - TSS,
distToAnchor2 = (start2 + end2)/2 - TSS,
distToAnchor = if_else(abs(distToAnchor1) > abs(distToAnchor2), distToAnchor1, distToAnchor2),
direction = if_else(distToAnchor > 0, \right\, \left\))
result <- data %>%
group_by(gene, direction) %>%
mutate(distToAnchor = abs(distToAnchor)) %>%
mutate(max_dist_P_S = ifelse(any(Anno2 == \P-S\),
max(distToAnchor[Anno2 == \P-S\], na.rm = TRUE),
NA)) %>%
mutate(is_smaller = ifelse(is.na(max_dist_P_S), NA,
ifelse(distToAnchor == max_dist_P_S, NA, distToAnchor < max_dist_P_S))) %>%
ungroup()
result2 <- result %>% dplyr::filter(!is.na(is_smaller))
result3 <- result2 %>% group_by(gene, direction) %>%
summarize(count_true = sum(is_smaller, na.rm = TRUE),
count_false = sum(is_smaller == FALSE, na.rm = TRUE)) %>%
dplyr::mutate(count = count_true + count_false,
perc = count_true/count*100) %>%
dplyr::select(gene, direction, perc)
result3 <- result3 %>% group_by(gene) %>% summarize(perc = mean(perc))
####### DOing this for P-S groups
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% psOver0 ~ \psOver0\,
gene %in% psOver1 ~ \psOver1\,
gene %in% psOver2 ~ \psOver2\,
gene %in% psOver3 ~ \psOver3\,
gene %in% psOver4 ~ \psOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
####### DOing this for P-E groups
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% peOver0 ~ \peOver0\,
gene %in% peOver1 ~ \peOver1\,
gene %in% peOver2 ~ \peOver2\,
gene %in% peOver3 ~ \peOver3\,
gene %in% peOver4 ~ \peOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
####### DOing this for P-P groups
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \ppOver0\,
gene %in% ppOver1 ~ \ppOver1\,
gene %in% ppOver2 ~ \ppOver2\,
gene %in% ppOver3 ~ \ppOver3\,
gene %in% ppOver4 ~ \ppOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.32] Checking directionality
<!-- rnb-text-end -->
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n\nresult <- data %>%\n  group_by(gene, direction) %>%\n  mutate(distToAnchor = abs(distToAnchor)) %>%\n  mutate(max_dist_P_S = ifelse(any(Anno2 == \\P-S\\), \n                               max(distToAnchor[Anno2 == \\P-S\\], na.rm = TRUE), \n                               NA))\nresult <- result %>% dplyr::filter(Anno2 != \\P-S\\) %>%\n  mutate(is_smaller = ifelse(is.na(max_dist_P_S), NA, \n                             ifelse(distToAnchor == max_dist_P_S, NA, distToAnchor < max_dist_P_S))) %>%\n  ungroup()\n\n\n\nresult2 <- result %>% dplyr::filter(!is.na(is_smaller))\n\nresult3 <- result2 %>% group_by(gene, direction) %>%\n  summarize(count_true = sum(is_smaller, na.rm = TRUE),\n            count_false = sum(is_smaller == FALSE, na.rm = TRUE)) %>%\n  dplyr::mutate(count = count_true + count_false,\n                perc = count_true/count*100) %>%\n  dplyr::select(gene, direction, perc)\n\nresult3 <- result3 %>% group_by(gene) %>% summarize(perc = mean(perc))\n\n####### DOing this for P-S groups\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% psOver0 ~ \\psOver0\\,\n                                            gene %in% psOver1 ~ \\psOver1\\,\n                                            gene %in% psOver2 ~ \\psOver2\\,\n                                            gene %in% psOver3 ~ \\psOver3\\,\n                                            gene %in% psOver4 ~ \\psOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n####### DOing this for P-E groups\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% peOver0 ~ \\peOver0\\,\n                                            gene %in% peOver1 ~ \\peOver1\\,\n                                            gene %in% peOver2 ~ \\peOver2\\,\n                                            gene %in% peOver3 ~ \\peOver3\\,\n                                            gene %in% peOver4 ~ \\peOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n####### DOing this for P-P groups\n\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \\ppOver0\\,\n                                            gene %in% ppOver1 ~ \\ppOver1\\,\n                                            gene %in% ppOver2 ~ \\ppOver2\\,\n                                            gene %in% ppOver3 ~ \\ppOver3\\,\n                                            gene %in% ppOver4 ~ \\ppOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n```\n\n<!-- rnb-source-end -->\n"} -->
result <- data %>%
group_by(gene, direction) %>%
mutate(distToAnchor = abs(distToAnchor)) %>%
mutate(max_dist_P_S = ifelse(any(Anno2 == \P-S\),
max(distToAnchor[Anno2 == \P-S\], na.rm = TRUE),
NA))
result <- result %>% dplyr::filter(Anno2 != \P-S\) %>%
mutate(is_smaller = ifelse(is.na(max_dist_P_S), NA,
ifelse(distToAnchor == max_dist_P_S, NA, distToAnchor < max_dist_P_S))) %>%
ungroup()
result2 <- result %>% dplyr::filter(!is.na(is_smaller))
result3 <- result2 %>% group_by(gene, direction) %>%
summarize(count_true = sum(is_smaller, na.rm = TRUE),
count_false = sum(is_smaller == FALSE, na.rm = TRUE)) %>%
dplyr::mutate(count = count_true + count_false,
perc = count_true/count*100) %>%
dplyr::select(gene, direction, perc)
result3 <- result3 %>% group_by(gene) %>% summarize(perc = mean(perc))
####### DOing this for P-S groups
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% psOver0 ~ \psOver0\,
gene %in% psOver1 ~ \psOver1\,
gene %in% psOver2 ~ \psOver2\,
gene %in% psOver3 ~ \psOver3\,
gene %in% psOver4 ~ \psOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
####### DOing this for P-E groups
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% peOver0 ~ \peOver0\,
gene %in% peOver1 ~ \peOver1\,
gene %in% peOver2 ~ \peOver2\,
gene %in% peOver3 ~ \peOver3\,
gene %in% peOver4 ~ \peOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
####### DOing this for P-P groups
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \ppOver0\,
gene %in% ppOver1 ~ \ppOver1\,
gene %in% ppOver2 ~ \ppOver2\,
gene %in% ppOver3 ~ \ppOver3\,
gene %in% ppOver4 ~ \ppOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
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-->\n\n```r\n```r\n\nresult <- data %>%\n  group_by(gene, direction) %>%\n  mutate(distToAnchor = abs(distToAnchor)) %>%\n  mutate(max_dist_P_S = ifelse(any(Anno2 == \\P-S\\), \n                               max(distToAnchor[Anno2 == \\P-S\\], na.rm = TRUE), \n                               NA))\nresult <- result %>% dplyr::filter(Anno2 != \\P-S\\) %>%\n  mutate(is_smaller = ifelse(is.na(max_dist_P_S), NA, \n                             ifelse(distToAnchor == max_dist_P_S, NA, distToAnchor < max_dist_P_S))) %>%\n  ungroup()\n\n\n\nresult2 <- result %>% dplyr::filter(!is.na(is_smaller))\n\nresult3 <- result2 %>% group_by(gene, direction) %>%\n  summarize(count_true = sum(is_smaller, na.rm = TRUE),\n            count_false = sum(is_smaller == FALSE, na.rm = TRUE)) %>%\n  dplyr::mutate(count = count_true + count_false,\n                perc = count_true/count*100) %>%\n  dplyr::select(gene, direction, perc)\n\nresult3 <- result3 %>% group_by(gene) %>% summarize(perc = mean(perc))\n\n####### DOing this for P-S groups\nresultDir <- here(\\../../result\\)\ntemp2 <- readRDS(here(resultDir, \\gene_loop_link.rds\\))\n\npsOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene\npsOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene\npsOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene\npsOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene\npsOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% psOver0 ~ \\psOver0\\,\n                                            gene %in% psOver1 ~ \\psOver1\\,\n                                            gene %in% psOver2 ~ \\psOver2\\,\n                                            gene %in% psOver3 ~ \\psOver3\\,\n                                            gene %in% psOver4 ~ \\psOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n####### DOing this for P-E groups\npeOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene\npeOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene\npeOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene\npeOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene\npeOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% peOver0 ~ \\peOver0\\,\n                                            gene %in% peOver1 ~ \\peOver1\\,\n                                            gene %in% peOver2 ~ \\peOver2\\,\n                                            gene %in% peOver3 ~ \\peOver3\\,\n                                            gene %in% peOver4 ~ \\peOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n####### DOing this for P-P groups\n\nppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene\nppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene\nppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene\nppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene\nppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene\n\n\nresult4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \\ppOver0\\,\n                                            gene %in% ppOver1 ~ \\ppOver1\\,\n                                            gene %in% ppOver2 ~ \\ppOver2\\,\n                                            gene %in% ppOver3 ~ \\ppOver3\\,\n                                            gene %in% ppOver4 ~ \\ppOver4\\,\n                                            TRUE ~ NA)) %>%\n  dplyr::filter(!is.na(group))\n\n\nggplot(result4, aes(x =  group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +\n  stat_summary(aes(group = group), fun = mean, geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\)\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
result <- data %>%
group_by(gene, direction) %>%
mutate(distToAnchor = abs(distToAnchor)) %>%
mutate(max_dist_P_S = ifelse(any(Anno2 == \P-S\),
max(distToAnchor[Anno2 == \P-S\], na.rm = TRUE),
NA))
result <- result %>% dplyr::filter(Anno2 != \P-S\) %>%
mutate(is_smaller = ifelse(is.na(max_dist_P_S), NA,
ifelse(distToAnchor == max_dist_P_S, NA, distToAnchor < max_dist_P_S))) %>%
ungroup()
result2 <- result %>% dplyr::filter(!is.na(is_smaller))
result3 <- result2 %>% group_by(gene, direction) %>%
summarize(count_true = sum(is_smaller, na.rm = TRUE),
count_false = sum(is_smaller == FALSE, na.rm = TRUE)) %>%
dplyr::mutate(count = count_true + count_false,
perc = count_true/count*100) %>%
dplyr::select(gene, direction, perc)
result3 <- result3 %>% group_by(gene) %>% summarize(perc = mean(perc))
####### DOing this for P-S groups
resultDir <- here(\../../result\)
temp2 <- readRDS(here(resultDir, \gene_loop_link.rds\))
psOver4 <- (temp2 %>% dplyr::filter(num_ps >= 4))$gene
psOver3 <- (temp2 %>% dplyr::filter(num_ps >= 3, num_ps < 4))$gene
psOver2 <- (temp2 %>% dplyr::filter(num_ps >= 2, num_ps < 3))$gene
psOver1 <- (temp2 %>% dplyr::filter(num_ps >= 1, num_ps < 2))$gene
psOver0 <- (temp2 %>% dplyr::filter(num_ps < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% psOver0 ~ \psOver0\,
gene %in% psOver1 ~ \psOver1\,
gene %in% psOver2 ~ \psOver2\,
gene %in% psOver3 ~ \psOver3\,
gene %in% psOver4 ~ \psOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
####### DOing this for P-E groups
peOver4 <- (temp2 %>% dplyr::filter(num_pe >= 4))$gene
peOver3 <- (temp2 %>% dplyr::filter(num_pe >= 3, num_pe < 4))$gene
peOver2 <- (temp2 %>% dplyr::filter(num_pe >= 2, num_pe < 3))$gene
peOver1 <- (temp2 %>% dplyr::filter(num_pe >= 1, num_pe < 2))$gene
peOver0 <- (temp2 %>% dplyr::filter(num_pe < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% peOver0 ~ \peOver0\,
gene %in% peOver1 ~ \peOver1\,
gene %in% peOver2 ~ \peOver2\,
gene %in% peOver3 ~ \peOver3\,
gene %in% peOver4 ~ \peOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
####### DOing this for P-P groups
ppOver4 <- (temp2 %>% dplyr::filter(num_pp >= 4))$gene
ppOver3 <- (temp2 %>% dplyr::filter(num_pp >= 3, num_pp < 4))$gene
ppOver2 <- (temp2 %>% dplyr::filter(num_pp >= 2, num_pp < 3))$gene
ppOver1 <- (temp2 %>% dplyr::filter(num_pp >= 1, num_pp < 2))$gene
ppOver0 <- (temp2 %>% dplyr::filter(num_pp < 1))$gene
result4 <- result3 %>% dplyr::mutate(group = case_when(gene %in% ppOver0 ~ \ppOver0\,
gene %in% ppOver1 ~ \ppOver1\,
gene %in% ppOver2 ~ \ppOver2\,
gene %in% ppOver3 ~ \ppOver3\,
gene %in% ppOver4 ~ \ppOver4\,
TRUE ~ NA)) %>%
dplyr::filter(!is.na(group))
ggplot(result4, aes(x = group, y = perc)) + geom_violin() + geom_boxplot(width = 0.05) + theme_classic() +
stat_summary(aes(group = group), fun = mean, geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.33] Checking guidance
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peak.CTCF <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
library(BSgenome.Mmusculus.UCSC.mm10)
# Extract sequences
sequences <- getSeq(BSgenome.Mmusculus.UCSC.mm10, peak.CTCF)
# Get the CTCF motif (or provide your own PWM)
ctcf_motif <- query(MotifDb, c(\CTCF\, \Mmusculus\))[[1]]
# Scan for the motif
motif_hits <- lapply(sequences, function(seq) {
matchPWM(ctcf_motif, seq, min.score = \80%\) # Adjust min.score as needed
})
motif_hits_reverse <- lapply(sequences, function(seq) {
matchPWM(ctcf_motif, reverseComplement(seq), min.score = \80%\)
})
# Combine results
result <- mapply(function(fwd, rev) {
list(forward = fwd, reverse = rev)
}, motif_hits, motif_hits_reverse, SIMPLIFY = FALSE)
result[[5]]
result_tibble <- tibble(
peak_id = seq_along(result), # Create an identifier for each peak
alignment = map_chr(result, function(res) {
has_fwd <- length(res$forward) > 0
has_rev <- length(res$reverse) > 0
if (has_fwd && has_rev) \fwdrev\
else if (has_fwd) \fwd\
else if (has_rev) \rev\
else \none\
})
)
temp <- as_tibble(peak.CTCF) %>% bind_cols(result_tibble) %>%
dplyr::select(seqnames, start, end, alignment)
fwrite(temp, here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\), col.names = FALSE, sep = \\t\)
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```r
```r
peak.CTCF <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
library(BSgenome.Mmusculus.UCSC.mm10)
# Extract sequences
sequences <- getSeq(BSgenome.Mmusculus.UCSC.mm10, peak.CTCF)
# Get the CTCF motif (or provide your own PWM)
ctcf_motif <- query(MotifDb, c(\CTCF\, \Mmusculus\))[[1]]
# Scan for the motif
motif_hits <- lapply(sequences, function(seq) {
matchPWM(ctcf_motif, seq, min.score = \80%\) # Adjust min.score as needed
})
motif_hits_reverse <- lapply(sequences, function(seq) {
matchPWM(ctcf_motif, reverseComplement(seq), min.score = \80%\)
})
# Combine results
result <- mapply(function(fwd, rev) {
list(forward = fwd, reverse = rev)
}, motif_hits, motif_hits_reverse, SIMPLIFY = FALSE)
result[[5]]
result_tibble <- tibble(
peak_id = seq_along(result), # Create an identifier for each peak
alignment = map_chr(result, function(res) {
has_fwd <- length(res$forward) > 0
has_rev <- length(res$reverse) > 0
if (has_fwd && has_rev) \fwdrev\
else if (has_fwd) \fwd\
else if (has_rev) \rev\
else \none\
})
)
temp <- as_tibble(peak.CTCF) %>% bind_cols(result_tibble) %>%
dplyr::select(seqnames, start, end, alignment)
fwrite(temp, here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\), col.names = FALSE, sep = \\t\)
```
```
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-->\n\n```r\nctcf.peak <- fread(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\\))\ncolnames(ctcf.peak) <- c(\\chr\\, \\start\\, \\end\\, \\motif\\)\nctcf.peak.gr <- makeGRangesFromDataFrame(ctcf.peak, keep.extra.columns = TRUE)\n\n\nflankSize <- 1\ngene.TSS.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)\ncolnames(gene.TSS.tb) <- c(\\chr\\, \\start\\, \\end\\, \\strand\\, \\gene\\, \\ensembl\\)\n\ntss.gr <- makeGRangesFromDataFrame(gene.TSS.tb, keep.extra.columns = TRUE)\n\nfwd_peaks <- ctcf.peak.gr[ctcf.peak.gr$motif %in% c(\\fwd\\, \\fwdrev\\)]\nrev_peaks <- ctcf.peak.gr[ctcf.peak.gr$motif %in% c(\\rev\\, \\fwdrev\\)]\n\n# Find convergent CTCF pairs for each TSS\nconvergent_pairs <- lapply(seq_along(tss.gr), function(i) {\n    current_tss <- tss.gr[i]\n    \n    # Filter peaks on the same chromosome as the TSS\n    fwd_peaks_chr <- fwd_peaks[as.character(seqnames(fwd_peaks)) == as.character(seqnames(current_tss))]\n    rev_peaks_chr <- rev_peaks[as.character(seqnames(rev_peaks)) == as.character(seqnames(current_tss))]\n    \n    # Find the closest forward peak to the left of the TSS\n    left_fwd <- fwd_peaks_chr[start(fwd_peaks_chr) < start(current_tss)]\n    closest_fwd <- if (length(left_fwd) > 0) {\n      left_fwd[which.max(start(left_fwd))]\n    } else {\n      NA\n    }\n    \n    # Find the closest reverse peak to the right of the TSS\n    right_rev <- rev_peaks_chr[start(rev_peaks_chr) > start(current_tss)]\n    closest_rev <- if (length(right_rev) > 0) {\n      right_rev[which.min(start(right_rev))]\n    } else {\n      NA\n    }\n    \n    # Combine results if both closest peaks exist\n    if (!is.na(closest_fwd) && !is.na(closest_rev)) {\n        tibble(\n          chrom1 = as.character(seqnames(closest_fwd)),\n          start1 = start(closest_fwd),\n          end1 = end(closest_fwd),\n          chrom2 = as.character(seqnames(closest_rev)),\n          start2 = start(closest_rev),\n          end2 = end(closest_rev),\n          ensembl = current_tss$ensembl,\n        )\n    } else {\n        NULL\n    }\n})\n\n# Combine results into a tibble\nresult_tibble <- bind_rows(convergent_pairs)\n\nfwrite(result_tibble, here(refDir, \\tss_convergent_ctcf_motif_pairs.bedpe\\), sep = \\\\t\\, col.names = FALSE)\nfwrite(gene.TSS.tb %>% dplyr::select(c(1, 2, 3, 5)), here(refDir, \\tss_2bp.bed\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- fread(here(refDir, \\tss_convergent_ctcf_motif_pairs.bedpe\\)) %>% dplyr::filter(V7 == \\Klf4\\)\n\nfwrite(as_tibble(temp), here(refDir, \\tss_convergent_ctcf_motif_pairs_klf4.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\n# Checking size distribution of ctcf pair\ntemp <- fread(here(refDir, \\tss_convergent_ctcf_motif_pairs.bedpe\\))\ntemp <- temp %>% dplyr::mutate(size = (V6 + V5)/2 - (V3 + V2)/2,\n                       id = paste(V1, V2, V3, V5, V6, sep = \\_\\))\ntemp <- temp %>% dplyr::select(id, size) %>% distinct()\n\nggplot(temp, aes(x = size)) + geom_histogram() + scale_x_continuous(labels = label_kb_mb, limits = c(0, 1e6)) + scale_y_log10() + theme_classic() + ggtitle(\\size distribution of smallest convergent ctcf pairs around gene\\)\n```\n\n<!-- rnb-source-end -->\n"} -->
ctcf.peak <- fread(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\))
colnames(ctcf.peak) <- c(\chr\, \start\, \end\, \motif\)
ctcf.peak.gr <- makeGRangesFromDataFrame(ctcf.peak, keep.extra.columns = TRUE)
flankSize <- 1
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)
colnames(gene.TSS.tb) <- c(\chr\, \start\, \end\, \strand\, \gene\, \ensembl\)
tss.gr <- makeGRangesFromDataFrame(gene.TSS.tb, keep.extra.columns = TRUE)
fwd_peaks <- ctcf.peak.gr[ctcf.peak.gr$motif %in% c(\fwd\, \fwdrev\)]
rev_peaks <- ctcf.peak.gr[ctcf.peak.gr$motif %in% c(\rev\, \fwdrev\)]
# Find convergent CTCF pairs for each TSS
convergent_pairs <- lapply(seq_along(tss.gr), function(i) {
current_tss <- tss.gr[i]
# Filter peaks on the same chromosome as the TSS
fwd_peaks_chr <- fwd_peaks[as.character(seqnames(fwd_peaks)) == as.character(seqnames(current_tss))]
rev_peaks_chr <- rev_peaks[as.character(seqnames(rev_peaks)) == as.character(seqnames(current_tss))]
# Find the closest forward peak to the left of the TSS
left_fwd <- fwd_peaks_chr[start(fwd_peaks_chr) < start(current_tss)]
closest_fwd <- if (length(left_fwd) > 0) {
left_fwd[which.max(start(left_fwd))]
} else {
NA
}
# Find the closest reverse peak to the right of the TSS
right_rev <- rev_peaks_chr[start(rev_peaks_chr) > start(current_tss)]
closest_rev <- if (length(right_rev) > 0) {
right_rev[which.min(start(right_rev))]
} else {
NA
}
# Combine results if both closest peaks exist
if (!is.na(closest_fwd) && !is.na(closest_rev)) {
tibble(
chrom1 = as.character(seqnames(closest_fwd)),
start1 = start(closest_fwd),
end1 = end(closest_fwd),
chrom2 = as.character(seqnames(closest_rev)),
start2 = start(closest_rev),
end2 = end(closest_rev),
ensembl = current_tss$ensembl,
)
} else {
NULL
}
})
# Combine results into a tibble
result_tibble <- bind_rows(convergent_pairs)
fwrite(result_tibble, here(refDir, \tss_convergent_ctcf_motif_pairs.bedpe\), sep = \\t\, col.names = FALSE)
fwrite(gene.TSS.tb %>% dplyr::select(c(1, 2, 3, 5)), here(refDir, \tss_2bp.bed\), sep = \\t\, col.names = FALSE)
temp <- fread(here(refDir, \tss_convergent_ctcf_motif_pairs.bedpe\)) %>% dplyr::filter(V7 == \Klf4\)
fwrite(as_tibble(temp), here(refDir, \tss_convergent_ctcf_motif_pairs_klf4.bedpe\), sep = \\t\, col.names = FALSE)
# Checking size distribution of ctcf pair
temp <- fread(here(refDir, \tss_convergent_ctcf_motif_pairs.bedpe\))
temp <- temp %>% dplyr::mutate(size = (V6 + V5)/2 - (V3 + V2)/2,
id = paste(V1, V2, V3, V5, V6, sep = \_\))
temp <- temp %>% dplyr::select(id, size) %>% distinct()
ggplot(temp, aes(x = size)) + geom_histogram() + scale_x_continuous(labels = label_kb_mb, limits = c(0, 1e6)) + scale_y_log10() + theme_classic() + ggtitle(\size distribution of smallest convergent ctcf pairs around gene\)
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-->\n\n```r\n```r\nctcf.peak <- fread(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\\))\ncolnames(ctcf.peak) <- c(\\chr\\, \\start\\, \\end\\, \\motif\\)\nctcf.peak.gr <- makeGRangesFromDataFrame(ctcf.peak, keep.extra.columns = TRUE)\n\n\nflankSize <- 1\ngene.TSS.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)\ncolnames(gene.TSS.tb) <- c(\\chr\\, \\start\\, \\end\\, \\strand\\, \\gene\\, \\ensembl\\)\n\ntss.gr <- makeGRangesFromDataFrame(gene.TSS.tb, keep.extra.columns = TRUE)\n\nfwd_peaks <- ctcf.peak.gr[ctcf.peak.gr$motif %in% c(\\fwd\\, \\fwdrev\\)]\nrev_peaks <- ctcf.peak.gr[ctcf.peak.gr$motif %in% c(\\rev\\, \\fwdrev\\)]\n\n# Find convergent CTCF pairs for each TSS\nconvergent_pairs <- lapply(seq_along(tss.gr), function(i) {\n    current_tss <- tss.gr[i]\n    \n    # Filter peaks on the same chromosome as the TSS\n    fwd_peaks_chr <- fwd_peaks[as.character(seqnames(fwd_peaks)) == as.character(seqnames(current_tss))]\n    rev_peaks_chr <- rev_peaks[as.character(seqnames(rev_peaks)) == as.character(seqnames(current_tss))]\n    \n    # Find the closest forward peak to the left of the TSS\n    left_fwd <- fwd_peaks_chr[start(fwd_peaks_chr) < start(current_tss)]\n    closest_fwd <- if (length(left_fwd) > 0) {\n      left_fwd[which.max(start(left_fwd))]\n    } else {\n      NA\n    }\n    \n    # Find the closest reverse peak to the right of the TSS\n    right_rev <- rev_peaks_chr[start(rev_peaks_chr) > start(current_tss)]\n    closest_rev <- if (length(right_rev) > 0) {\n      right_rev[which.min(start(right_rev))]\n    } else {\n      NA\n    }\n    \n    # Combine results if both closest peaks exist\n    if (!is.na(closest_fwd) && !is.na(closest_rev)) {\n        tibble(\n          chrom1 = as.character(seqnames(closest_fwd)),\n          start1 = start(closest_fwd),\n          end1 = end(closest_fwd),\n          chrom2 = as.character(seqnames(closest_rev)),\n          start2 = start(closest_rev),\n          end2 = end(closest_rev),\n          ensembl = current_tss$ensembl,\n        )\n    } else {\n        NULL\n    }\n})\n\n# Combine results into a tibble\nresult_tibble <- bind_rows(convergent_pairs)\n\nfwrite(result_tibble, here(refDir, \\tss_convergent_ctcf_motif_pairs.bedpe\\), sep = \\\\t\\, col.names = FALSE)\nfwrite(gene.TSS.tb %>% dplyr::select(c(1, 2, 3, 5)), here(refDir, \\tss_2bp.bed\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- fread(here(refDir, \\tss_convergent_ctcf_motif_pairs.bedpe\\)) %>% dplyr::filter(V7 == \\Klf4\\)\n\nfwrite(as_tibble(temp), here(refDir, \\tss_convergent_ctcf_motif_pairs_klf4.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\n# Checking size distribution of ctcf pair\ntemp <- fread(here(refDir, \\tss_convergent_ctcf_motif_pairs.bedpe\\))\ntemp <- temp %>% dplyr::mutate(size = (V6 + V5)/2 - (V3 + V2)/2,\n                       id = paste(V1, V2, V3, V5, V6, sep = \\_\\))\ntemp <- temp %>% dplyr::select(id, size) %>% distinct()\n\nggplot(temp, aes(x = size)) + geom_histogram() + scale_x_continuous(labels = label_kb_mb, limits = c(0, 1e6)) + scale_y_log10() + theme_classic() + ggtitle(\\size distribution of smallest convergent ctcf pairs around gene\\)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
ctcf.peak <- fread(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\))
colnames(ctcf.peak) <- c(\chr\, \start\, \end\, \motif\)
ctcf.peak.gr <- makeGRangesFromDataFrame(ctcf.peak, keep.extra.columns = TRUE)
flankSize <- 1
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)
colnames(gene.TSS.tb) <- c(\chr\, \start\, \end\, \strand\, \gene\, \ensembl\)
tss.gr <- makeGRangesFromDataFrame(gene.TSS.tb, keep.extra.columns = TRUE)
fwd_peaks <- ctcf.peak.gr[ctcf.peak.gr$motif %in% c(\fwd\, \fwdrev\)]
rev_peaks <- ctcf.peak.gr[ctcf.peak.gr$motif %in% c(\rev\, \fwdrev\)]
# Find convergent CTCF pairs for each TSS
convergent_pairs <- lapply(seq_along(tss.gr), function(i) {
current_tss <- tss.gr[i]
# Filter peaks on the same chromosome as the TSS
fwd_peaks_chr <- fwd_peaks[as.character(seqnames(fwd_peaks)) == as.character(seqnames(current_tss))]
rev_peaks_chr <- rev_peaks[as.character(seqnames(rev_peaks)) == as.character(seqnames(current_tss))]
# Find the closest forward peak to the left of the TSS
left_fwd <- fwd_peaks_chr[start(fwd_peaks_chr) < start(current_tss)]
closest_fwd <- if (length(left_fwd) > 0) {
left_fwd[which.max(start(left_fwd))]
} else {
NA
}
# Find the closest reverse peak to the right of the TSS
right_rev <- rev_peaks_chr[start(rev_peaks_chr) > start(current_tss)]
closest_rev <- if (length(right_rev) > 0) {
right_rev[which.min(start(right_rev))]
} else {
NA
}
# Combine results if both closest peaks exist
if (!is.na(closest_fwd) && !is.na(closest_rev)) {
tibble(
chrom1 = as.character(seqnames(closest_fwd)),
start1 = start(closest_fwd),
end1 = end(closest_fwd),
chrom2 = as.character(seqnames(closest_rev)),
start2 = start(closest_rev),
end2 = end(closest_rev),
ensembl = current_tss$ensembl,
)
} else {
NULL
}
})
# Combine results into a tibble
result_tibble <- bind_rows(convergent_pairs)
fwrite(result_tibble, here(refDir, \tss_convergent_ctcf_motif_pairs.bedpe\), sep = \\t\, col.names = FALSE)
fwrite(gene.TSS.tb %>% dplyr::select(c(1, 2, 3, 5)), here(refDir, \tss_2bp.bed\), sep = \\t\, col.names = FALSE)
temp <- fread(here(refDir, \tss_convergent_ctcf_motif_pairs.bedpe\)) %>% dplyr::filter(V7 == \Klf4\)
fwrite(as_tibble(temp), here(refDir, \tss_convergent_ctcf_motif_pairs_klf4.bedpe\), sep = \\t\, col.names = FALSE)
# Checking size distribution of ctcf pair
temp <- fread(here(refDir, \tss_convergent_ctcf_motif_pairs.bedpe\))
temp <- temp %>% dplyr::mutate(size = (V6 + V5)/2 - (V3 + V2)/2,
id = paste(V1, V2, V3, V5, V6, sep = \_\))
temp <- temp %>% dplyr::select(id, size) %>% distinct()
ggplot(temp, aes(x = size)) + geom_histogram() + scale_x_continuous(labels = label_kb_mb, limits = c(0, 1e6)) + scale_y_log10() + theme_classic() + ggtitle(\size distribution of smallest convergent ctcf pairs around gene\)
```
```
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<!-- rnb-text-begin -->
### [2.34] Checking CTCF motif orientation
#### Calculating CTCF motif
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nflankSize <- 1\ngene.TSS.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)\ncolnames(gene.TSS.tb) <- c(\\chr\\, \\start\\, \\end\\, \\strand\\, \\gene\\, \\ensembl\\)\ngeneEnsemblPair <- gene.TSS.tb %>% dplyr::select(gene, ensembl)\n\nboundary.pair <- fread(here(refDir, \\tss_convergent_ctcf_motif_pairs.bedpe\\))\ncolnames(boundary.pair) <- c(\\boundary_chrom1\\, \\boundary_start1\\, \\boundary_end1\\, \\boundary_chrom2\\, \\boundary_start2\\, \\boundary_end2\\, \\ensembl\\)\nboundary.pair <- boundary.pair %>% dplyr::left_join(geneEnsemblPair, by = c(\\ensembl\\))\n\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\)) %>%\n  dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 24, 29, 31)) %>% unnest(gene)\n\n\ndata <- geneAnnoData %>% dplyr::left_join(boundary.pair, by = c(\\gene\\ = \\ensembl\\))\n\ndata <- data %>% rowwise() %>% dplyr::mutate(isWithinLeftBd = (boundary_start1 <= min(end1, end2)),\n                       isWithinRightBd = (boundary_end2 >= max(start1, start2)),\n                       isWithinBd = isWithinLeftBd & isWithinRightBd)\ndata <- data %>% drop_na()\n\n\nggplot(data, aes(x = DMSO, y = dTAG)) + geom_point() + coord_fixed() +  \n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  theme_classic() + facet_wrap(~ isWithinBd)\n\nggplot(data, aes(x = isWithinBd, y = diff_dTAG_DMSO, fill = isWithinBd)) + geom_violin() + geom_boxplot(width = 0.1) + theme_classic() +\n  geom_hline(yintercept = 0)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(isWithinBd ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(isWithinBd ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetPvalWilcox(data, TRUE, FALSE)\n\n\n# Proportion of those gene between group1 and group2\ndata.isWithinBd <- data %>% dplyr::filter(isWithinBd)\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nsum(gene.group1 %in% data.isWithinBd$gene)/length(gene.group1)*100\n\nsum(gene.group2 %in% data.isWithinBd$gene)/length(gene.group2)*100\n\n# Checking size of those loops\ndata <- data %>% dplyr::mutate(distance = start2 - start1)\n\nggplot(data, aes(x = isWithinBd, y = distance, fill = isWithinBd)) + geom_violin() + geom_boxplot(width = 0.1) + theme_classic()\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
flankSize <- 1
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)
colnames(gene.TSS.tb) <- c(\chr\, \start\, \end\, \strand\, \gene\, \ensembl\)
geneEnsemblPair <- gene.TSS.tb %>% dplyr::select(gene, ensembl)
boundary.pair <- fread(here(refDir, \tss_convergent_ctcf_motif_pairs.bedpe\))
colnames(boundary.pair) <- c(\boundary_chrom1\, \boundary_start1\, \boundary_end1\, \boundary_chrom2\, \boundary_start2\, \boundary_end2\, \ensembl\)
boundary.pair <- boundary.pair %>% dplyr::left_join(geneEnsemblPair, by = c(\ensembl\))
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\)) %>%
dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 24, 29, 31)) %>% unnest(gene)
data <- geneAnnoData %>% dplyr::left_join(boundary.pair, by = c(\gene\ = \ensembl\))
data <- data %>% rowwise() %>% dplyr::mutate(isWithinLeftBd = (boundary_start1 <= min(end1, end2)),
isWithinRightBd = (boundary_end2 >= max(start1, start2)),
isWithinBd = isWithinLeftBd & isWithinRightBd)
data <- data %>% drop_na()
ggplot(data, aes(x = DMSO, y = dTAG)) + geom_point() + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
theme_classic() + facet_wrap(~ isWithinBd)
ggplot(data, aes(x = isWithinBd, y = diff_dTAG_DMSO, fill = isWithinBd)) + geom_violin() + geom_boxplot(width = 0.1) + theme_classic() +
geom_hline(yintercept = 0)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(isWithinBd ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(isWithinBd ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
getPvalWilcox(data, TRUE, FALSE)
# Proportion of those gene between group1 and group2
data.isWithinBd <- data %>% dplyr::filter(isWithinBd)
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
sum(gene.group1 %in% data.isWithinBd$gene)/length(gene.group1)*100
sum(gene.group2 %in% data.isWithinBd$gene)/length(gene.group2)*100
# Checking size of those loops
data <- data %>% dplyr::mutate(distance = start2 - start1)
ggplot(data, aes(x = isWithinBd, y = distance, fill = isWithinBd)) + geom_violin() + geom_boxplot(width = 0.1) + theme_classic()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nflankSize <- 1\ngene.TSS.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)\ncolnames(gene.TSS.tb) <- c(\\chr\\, \\start\\, \\end\\, \\strand\\, \\gene\\, \\ensembl\\)\ngeneEnsemblPair <- gene.TSS.tb %>% dplyr::select(gene, ensembl)\n\nboundary.pair <- fread(here(refDir, \\tss_convergent_ctcf_motif_pairs.bedpe\\))\ncolnames(boundary.pair) <- c(\\boundary_chrom1\\, \\boundary_start1\\, \\boundary_end1\\, \\boundary_chrom2\\, \\boundary_start2\\, \\boundary_end2\\, \\ensembl\\)\nboundary.pair <- boundary.pair %>% dplyr::left_join(geneEnsemblPair, by = c(\\ensembl\\))\n\n\n# Importing loop gene annotation\nname <- \\chromo_cons_annoHierarchy\\\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\)) %>%\n  dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 24, 29, 31)) %>% unnest(gene)\n\n\ndata <- geneAnnoData %>% dplyr::left_join(boundary.pair, by = c(\\gene\\ = \\ensembl\\))\n\ndata <- data %>% rowwise() %>% dplyr::mutate(isWithinLeftBd = (boundary_start1 <= min(end1, end2)),\n                       isWithinRightBd = (boundary_end2 >= max(start1, start2)),\n                       isWithinBd = isWithinLeftBd & isWithinRightBd)\ndata <- data %>% drop_na()\n\n\nggplot(data, aes(x = DMSO, y = dTAG)) + geom_point() + coord_fixed() +  \n  geom_abline(slope = 1, intercept = 0, col = \\grey50\\, linetype = \\dashed\\) +\n  geom_hline(yintercept = 0, alpha = 0.5, color = \\grey\\) +\n  geom_vline(xintercept = 0, alpha = 0.5, color = \\grey\\) +\n  theme_classic() + facet_wrap(~ isWithinBd)\n\nggplot(data, aes(x = isWithinBd, y = diff_dTAG_DMSO, fill = isWithinBd)) + geom_violin() + geom_boxplot(width = 0.1) + theme_classic() +\n  geom_hline(yintercept = 0)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(isWithinBd ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(isWithinBd ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetPvalWilcox(data, TRUE, FALSE)\n\n\n# Proportion of those gene between group1 and group2\ndata.isWithinBd <- data %>% dplyr::filter(isWithinBd)\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nsum(gene.group1 %in% data.isWithinBd$gene)/length(gene.group1)*100\n\nsum(gene.group2 %in% data.isWithinBd$gene)/length(gene.group2)*100\n\n# Checking size of those loops\ndata <- data %>% dplyr::mutate(distance = start2 - start1)\n\nggplot(data, aes(x = isWithinBd, y = distance, fill = isWithinBd)) + geom_violin() + geom_boxplot(width = 0.1) + theme_classic()\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
flankSize <- 1
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V4, V5, V6)
colnames(gene.TSS.tb) <- c(\chr\, \start\, \end\, \strand\, \gene\, \ensembl\)
geneEnsemblPair <- gene.TSS.tb %>% dplyr::select(gene, ensembl)
boundary.pair <- fread(here(refDir, \tss_convergent_ctcf_motif_pairs.bedpe\))
colnames(boundary.pair) <- c(\boundary_chrom1\, \boundary_start1\, \boundary_end1\, \boundary_chrom2\, \boundary_start2\, \boundary_end2\, \ensembl\)
boundary.pair <- boundary.pair %>% dplyr::left_join(geneEnsemblPair, by = c(\ensembl\))
# Importing loop gene annotation
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\)) %>%
dplyr::select(c(1, 2, 3, 4, 5, 6, 11, 12, 24, 29, 31)) %>% unnest(gene)
data <- geneAnnoData %>% dplyr::left_join(boundary.pair, by = c(\gene\ = \ensembl\))
data <- data %>% rowwise() %>% dplyr::mutate(isWithinLeftBd = (boundary_start1 <= min(end1, end2)),
isWithinRightBd = (boundary_end2 >= max(start1, start2)),
isWithinBd = isWithinLeftBd & isWithinRightBd)
data <- data %>% drop_na()
ggplot(data, aes(x = DMSO, y = dTAG)) + geom_point() + coord_fixed() +
geom_abline(slope = 1, intercept = 0, col = \grey50\, linetype = \dashed\) +
geom_hline(yintercept = 0, alpha = 0.5, color = \grey\) +
geom_vline(xintercept = 0, alpha = 0.5, color = \grey\) +
theme_classic() + facet_wrap(~ isWithinBd)
ggplot(data, aes(x = isWithinBd, y = diff_dTAG_DMSO, fill = isWithinBd)) + geom_violin() + geom_boxplot(width = 0.1) + theme_classic() +
geom_hline(yintercept = 0)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(isWithinBd ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(isWithinBd ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
getPvalWilcox(data, TRUE, FALSE)
# Proportion of those gene between group1 and group2
data.isWithinBd <- data %>% dplyr::filter(isWithinBd)
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
sum(gene.group1 %in% data.isWithinBd$gene)/length(gene.group1)*100
sum(gene.group2 %in% data.isWithinBd$gene)/length(gene.group2)*100
# Checking size of those loops
data <- data %>% dplyr::mutate(distance = start2 - start1)
ggplot(data, aes(x = isWithinBd, y = distance, fill = isWithinBd)) + geom_violin() + geom_boxplot(width = 0.1) + theme_classic()
```
```
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#### Comparing it to TSS
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Z3JvdXAxKSoxMDBcbmxlbmd0aCh1bmlxdWUocXVlcnlIaXRzKGZpbmRPdmVybGFwcyhUU1MxbWIuZ3JvdXAyLmdyLCBjdGNmLnBlYWsuZ3IpKSkpL2xlbmd0aChnZW5lLmdyb3VwMikqMTAwXG5cblxuXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= 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flankSize <- 2.5e3
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\, \gene\)
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
TSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)
TSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)
ctcf.peak <- fread(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\))
colnames(ctcf.peak) <- c(\chr\, \start\, \end\, \motif\)
ctcf.peak.gr <- makeGRangesFromDataFrame(ctcf.peak)
print(\+-2.5kb TSS overla with CTCF peak\)
length(unique(queryHits(findOverlaps(TSS1mb.group1.gr, ctcf.peak.gr))))/length(gene.group1)*100
length(unique(queryHits(findOverlaps(TSS1mb.group2.gr, ctcf.peak.gr))))/length(gene.group2)*100
ctcf.peak.gr <- makeGRangesFromDataFrame(ctcf.peak %>% dplyr::filter(motif != \none\))
print(\+-2.5kb TSS overla with CTCF peak with motif\)
length(unique(queryHits(findOverlaps(TSS1mb.group1.gr, ctcf.peak.gr))))/length(gene.group1)*100
length(unique(queryHits(findOverlaps(TSS1mb.group2.gr, ctcf.peak.gr))))/length(gene.group2)*100
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```r
```r
flankSize <- 2.5e3
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chr\, \start\, \end\, \ensembl\, \gene\)
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
TSS1mb.group1.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group1), keep.extra.columns = TRUE)
TSS1mb.group2.gr <- makeGRangesFromDataFrame(gene.tb %>% dplyr::filter(ensembl %in% gene.group2), keep.extra.columns = TRUE)
ctcf.peak <- fread(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\))
colnames(ctcf.peak) <- c(\chr\, \start\, \end\, \motif\)
ctcf.peak.gr <- makeGRangesFromDataFrame(ctcf.peak)
print(\+-2.5kb TSS overla with CTCF peak\)
length(unique(queryHits(findOverlaps(TSS1mb.group1.gr, ctcf.peak.gr))))/length(gene.group1)*100
length(unique(queryHits(findOverlaps(TSS1mb.group2.gr, ctcf.peak.gr))))/length(gene.group2)*100
ctcf.peak.gr <- makeGRangesFromDataFrame(ctcf.peak %>% dplyr::filter(motif != \none\))
print(\+-2.5kb TSS overla with CTCF peak with motif\)
length(unique(queryHits(findOverlaps(TSS1mb.group1.gr, ctcf.peak.gr))))/length(gene.group1)*100
length(unique(queryHits(findOverlaps(TSS1mb.group2.gr, ctcf.peak.gr))))/length(gene.group2)*100
```
```
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#### Filtering loops that fall within CTCF boundaries
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line(yintercept = -0.2,\n             alpha = 0.5, \n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\", linetype = \"dashed\") +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\"insulationBoundary_delta\")\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()  \n  \n  \n ###################### \n    \n    minValue <- -4\n  obsexp <- fread(here(consensusDir, paste0(\"loopScore_cons_obsexp.tsv\"))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\"id\"))\n\ndata <- geneAnnoData %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n# convPvalue(getPvalWilcox(data, \"noBoundary\", \"withinBoundary\"))\n# convPvalue(getPvalWilcox(data, \"noBoundary\", \"outsideBoundary\"))\n# convPvalue(getPvalWilcox(data, \"outsideBoundary\", \"withinBoundary\"))\n# \n\np <- ggplot(data, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \"square\", alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \"black\",\n               linewidth = lineThick * mmToLineUnit, lineend = \"square\",\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \"point\", shape = 21, size = 0.5,\n    fill = \"black\", color = \"black\", position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\"grey50\", \"#5EC962\", \"#5EC962\")) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \"log2(fc of obs/exp)\")  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\") + \n  coord_cartesian(ylim = c(-2, 2))\n    \nfileName <- paste0(\"insulationBoundary_obsexp\")\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\n\ngene.withinBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \"withinBoundary\"))$gene)\ngene.outsideBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \"outsideBoundary\"))$gene)\ngene.noBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \"noBoundary\"))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \"geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\"))$gene\ngene.group2 <- fread(here(refDir, \"geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\"))$gene\n\nprint(\"perc of gene with loop within Boundary\")\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\"perc of gene with loop outside Boundary\")\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\"perc of gene with loop with no Boundary\")\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \"diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\")) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \"withinBoundary\",\n    ensembl_gene_id %in% gene.noBoundary ~ \"noBoundary\",\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\n\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\nconvPvalue(getPvalWilcox(diff.RNA, \"withinBoundary\", \"noBoundary\"))\n\n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\nstat_ecdf(size = 0.4 ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \"Absolute log2(fold change)\",\n    y = \"Cumulative Probability\"\n  ) + coord_cartesian(xlim = c(0, 0.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\"log2FC_cdf_insulationBoundary\")\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Checking characterstics of anchors\ntemp1 <- anchor.data %>% dplyr::filter(group == \"noBoundary\") %>% dplyr::select(-group)\nanchor.noBoundary <- extractAnchor(temp1)\n\ntemp2 <- anchor.data %>% dplyr::filter(group == \"withinBoundary\") %>% dplyr::select(-group)\nanchor.withinBoundary <- extractAnchor(temp2)\n\ntemp3 <- anchor.data %>% dplyr::filter(group == \"outsideBoundary\") %>% dplyr::select(-group)\nanchor.outsideBoundary <- extractAnchor(temp3)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore\n  distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScoresBinary <- function(track, peak, name, anchor1, anchor2, anchor3){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor1)\n  b <- getSumScores(peakTrack, anchor2)\n  c <- getSumScores(peakTrack, anchor3)\n  a.tb <- tibble(loop = \"noBoundary\",\n                 sumScore = a)\n  b.tb <- tibble(loop = \"withBoundary\",\n                 sumScore = b)\n  c.tb <- tibble(loop = \"outsideBoundary\",\n                 sumScore = c)\n  \n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\"noBoundary\", \"withBoundary\", \"outsideBoundary\"))\n  \n  p12 <- getPvalWilcox(data, \"noBoundary\", \"withBoundary\")\n  p13 <- getPvalWilcox(data, \"noBoundary\", \"outsideBoundary\")\n  p23 <- getPvalWilcox(data, \"withBoundary\", \"outsideBoundary\")\n  p <-  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \"\\nlog(avg peak score per anchor)\")) +\n    geom_violin(aes(fill = loop), color = \"black\",\n                linewidth = lineThick * mmToLineUnit, lineend = \"square\",\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \"black\",\n                 linewidth = lineThick * mmToLineUnit, lineend = \"square\",\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \"point\", shape = 21, size = 1,\n      fill = \"red\", color = \"black\"\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeS, family = fontType, color = \"#000000\"\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \"#000000\"\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \"#000000\", size = lineThick * mmToLineUnit, lineend = \"square\"\n      ),\n      axis.ticks = element_line(\n        color = \"#000000\", size = lineThick * mmToLineUnit, lineend = \"square\"\n      ),\n      panel.background = element_rect(fill = \"transparent\"),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    #coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) + \n    annotate(\n      \"text\", x = 1, y = quantile(data$sumScore, 0.5),\n      label = paste0(\"p12: \", convPvalue(p12), \"\\n\",\n                     \"p13: \", convPvalue(p13), \"\\n\",\n                     \"p23: \", convPvalue(p23)),\n      color = \"black\", hjust = 0, size = 3\n    ) \n  #+ scale_y_continuous(trans = \"log10\")\n  \n  fileName <- paste0(\"ChIP_peak_avgPeakScore_boundaryAnchor_\", name)\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  png(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \"33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\"), format = \"BigWig\")\npeak <- importPeak(here(refDir, \"33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\"))\nplotSumScoresBinary(track, peak, \"H3K4me3\", anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \"GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\"), format = \"BigWig\")\npeak <- importPeak(here(refDir, \"GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\"))\n#plotSumScores(track, peak, \"H3K27ac\")\nplotSumScoresBinary(track, peak, \"H3K27ac\", anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \"GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\"), format = \"BigWig\")\npeak <- importPeak(here(refDir, \"GSM2683440_J1_H3K14ac_mm10Lifted.bed\"))\n#plotSumScores(track, peak, \"H3K14ac\")\nplotSumScoresBinary(track, peak, \"H3K14ac\", anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \"33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\"), format = \"BigWig\")\npeak <- importPeak(here(refDir, \"33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\"))\n#plotSumScores(track, peak, \"CTCF\")\nplotSumScoresBinary(track, peak, \"CTCF\", anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \"33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\"), format = \"BigWig\")\npeak <- importPeak(here(refDir, \"33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\"))\n#plotSumScores(track, peak, \"RAD21\")\nplotSumScoresBinary(track, peak, \"RAD21\", anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\n\n\n######## LOLA\natac <- fread(here(refDir, \"GSM3106257_ATAC_ESC_1.bed\")) %>% dplyr::select(V1, V2, V3)\ncolnames(atac) <- c(\"chr\", \"start\", \"end\")\natac.gr <- makeGRangesFromDataFrame(atac)\n\n# LOADING LOOPS\nloop.all <- fread(here(consensusDir, \"chromo_cons_annoHierarchy.tsv\")) %>% dplyr::filter(Anno2 %in% c(\"P-P\", \"P-E\", \"E-E\"))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\noverlaps <- findOverlaps(anchor.noBoundary, atac.gr)\nanchor.noBoundary <- pintersect(anchor.noBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.withinBoundary, atac.gr)\nanchor.withinBoundary <- pintersect(anchor.withinBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.outsideBoundary, atac.gr)\nanchor.outsideBoundary <- pintersect(anchor.outsideBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\n\nanchors <- list(anchor.noBoundary, anchor.withBoundary, anchor.outsideBoundary)\ntbs <- list()\ntemps <- list()\n\nalpha <- 0.05\n\n# Process clusters c1 to c8\nfor (i in 1:3) {\n  \n  anchor <- anchors[[i]]\n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \"embryonic stem cell\") %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\"target\", paste0(\"OR_c\", i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \"target\"), temps) %>%\n  mutate_all(~replace_na(., 1))\ncolnames(temp) <- c(\"target\", \"noBoundary\", \"withinBoundary\", \"outsideBoundary\")\ndata <- temp %>% column_to_rownames(\"target\") %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\"white\", \"red\"))\n\n\n#fviz_nbclust(data, kmeans, method = \"wss\")\n\np <- Heatmap(\n  data,\n  name = \"Odds Ratio\",                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```"} -->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>%\n  dplyr::select(c(1, 2, 3, 4, 5, 6, 7, 11, 12, 24, 29, 31)) %>% unnest(gene)\n\n\ngene.TSS.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V6, TSS)\ncolnames(gene.TSS.tb) <- c(\\ensembl\\, \\TSS\\)\n\n\ngeneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.TSS.tb, by = c(\\gene\\ = \\ensembl\\))\n\ngeneAnnoData <- geneAnnoData %>% \n  dplyr::mutate(distance1 = (start1 + end1)/2 - TSS,\n                distance2 = (start2 + end2)/2 - TSS,\n                distance = if_else(abs(distance1) > abs(distance2), distance1, distance2))\n\nresult <- geneAnnoData %>%\n  filter(Anno2 == \\P-S\\) %>%\n  group_by(gene) %>%\n  summarise(\n    farRight = if (any(distance > 0)) max(distance[distance > 0]) else 0,  # Return 0 if no positive distance\n    farLeft = if (any(distance < 0)) min(distance[distance < 0]) else 0  # Return 0 if no negative distance\n  ) %>%\n  ungroup()\n\ngeneAnnoData <- geneAnnoData %>% left_join(result, by = c(\\gene\\))\n\ngeneAnnoData <- geneAnnoData %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\)) %>%\n  dplyr::mutate(group = case_when(is.na(farRight) ~ \\No\\,\n                                  (distance > farLeft) & (distance < farRight) ~ \\Within\\,\n                                  TRUE ~ \\Outside\\),\n                size = start2 - start1)\n\ndata <- geneAnnoData %>% dplyr::select(group, id, size) %>% distinct()\nanchor.data <- geneAnnoData %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()\n\ntemp1 <- anchor.data %>% dplyr::filter(group == \\No\\) %>% dplyr::select(-group)\nfwrite(temp1, here(consensusDir, \\insulated_domain_ps_no.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp2 <- anchor.data %>% dplyr::filter(group == \\Within\\) %>% dplyr::select(-group)\nfwrite(temp2, here(consensusDir, \\insulated_domain_ps_within.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp3 <- anchor.data %>% dplyr::filter(group == \\Outside\\) %>% dplyr::select(-group)\nfwrite(temp3, here(consensusDir, \\insulated_domain_ps_outside.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$size\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$size\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n\n\np <- ggplot(data, aes(x = group, y = size, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n  stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Loop size\\)\n\nfileName <- paste0(\\insulationBoundary_size\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n  \n########################\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ndata <- geneAnnoData %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n\np <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\\insulationBoundary_delta\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n  \n  \n ###################### \n    \n    minValue <- -4\n  obsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\ndata <- geneAnnoData %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n# \n\np <- ggplot(data, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n    \nfileName <- paste0(\\insulationBoundary_obsexp\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\n\ngene.withinBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\withinBoundary\\))$gene)\ngene.outsideBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\outsideBoundary\\))$gene)\ngene.noBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\noBoundary\\))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nprint(\\perc of gene with loop within Boundary\\)\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop outside Boundary\\)\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop with no Boundary\\)\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \\withinBoundary\\,\n    ensembl_gene_id %in% gene.noBoundary ~ \\noBoundary\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\n\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\nconvPvalue(getPvalWilcox(diff.RNA, \\withinBoundary\\, \\noBoundary\\))\n\n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\nstat_ecdf(size = 0.4 ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 0.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\\log2FC_cdf_insulationBoundary\\)\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Checking characterstics of anchors\ntemp1 <- anchor.data %>% dplyr::filter(group == \\noBoundary\\) %>% dplyr::select(-group)\nanchor.noBoundary <- extractAnchor(temp1)\n\ntemp2 <- anchor.data %>% dplyr::filter(group == \\withinBoundary\\) %>% dplyr::select(-group)\nanchor.withinBoundary <- extractAnchor(temp2)\n\ntemp3 <- anchor.data %>% dplyr::filter(group == \\outsideBoundary\\) %>% dplyr::select(-group)\nanchor.outsideBoundary <- extractAnchor(temp3)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore\n  distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScoresBinary <- function(track, peak, name, anchor1, anchor2, anchor3){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor1)\n  b <- getSumScores(peakTrack, anchor2)\n  c <- getSumScores(peakTrack, anchor3)\n  a.tb <- tibble(loop = \\noBoundary\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\withBoundary\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\outsideBoundary\\,\n                 sumScore = c)\n  \n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\noBoundary\\, \\withBoundary\\, \\outsideBoundary\\))\n  \n  p12 <- getPvalWilcox(data, \\noBoundary\\, \\withBoundary\\)\n  p13 <- getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\)\n  p23 <- getPvalWilcox(data, \\withBoundary\\, \\outsideBoundary\\)\n  p <-  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\\\nlog(avg peak score per anchor)\\)) +\n    geom_violin(aes(fill = loop), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    #coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) + \n    annotate(\n      \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n      label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                     \\p13: \\, convPvalue(p13), \\\\n\\,\n                     \\p23: \\, convPvalue(p23)),\n      color = \\black\\, hjust = 0, size = 3\n    ) \n  #+ scale_y_continuous(trans = \\log10\\)\n  \n  fileName <- paste0(\\ChIP_peak_avgPeakScore_boundaryAnchor_\\, name)\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScoresBinary(track, peak, \\H3K4me3\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n#plotSumScores(track, peak, \\H3K27ac\\)\nplotSumScoresBinary(track, peak, \\H3K27ac\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\n#plotSumScores(track, peak, \\H3K14ac\\)\nplotSumScoresBinary(track, peak, \\H3K14ac\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\CTCF\\)\nplotSumScoresBinary(track, peak, \\CTCF\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\RAD21\\)\nplotSumScoresBinary(track, peak, \\RAD21\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\n\n\n######## LOLA\natac <- fread(here(refDir, \\GSM3106257_ATAC_ESC_1.bed\\)) %>% dplyr::select(V1, V2, V3)\ncolnames(atac) <- c(\\chr\\, \\start\\, \\end\\)\natac.gr <- makeGRangesFromDataFrame(atac)\n\n# LOADING LOOPS\nloop.all <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\noverlaps <- findOverlaps(anchor.noBoundary, atac.gr)\nanchor.noBoundary <- pintersect(anchor.noBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.withinBoundary, atac.gr)\nanchor.withinBoundary <- pintersect(anchor.withinBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.outsideBoundary, atac.gr)\nanchor.outsideBoundary <- pintersect(anchor.outsideBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\n\nanchors <- list(anchor.noBoundary, anchor.withBoundary, anchor.outsideBoundary)\ntbs <- list()\ntemps <- list()\n\nalpha <- 0.05\n\n# Process clusters c1 to c8\nfor (i in 1:3) {\n  \n  anchor <- anchors[[i]]\n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ncolnames(temp) <- c(\\target\\, \\noBoundary\\, \\withinBoundary\\, \\outsideBoundary\\)\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\)) %>%
dplyr::select(c(1, 2, 3, 4, 5, 6, 7, 11, 12, 24, 29, 31)) %>% unnest(gene)
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \TSS\)
geneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.TSS.tb, by = c(\gene\ = \ensembl\))
geneAnnoData <- geneAnnoData %>%
dplyr::mutate(distance1 = (start1 + end1)/2 - TSS,
distance2 = (start2 + end2)/2 - TSS,
distance = if_else(abs(distance1) > abs(distance2), distance1, distance2))
result <- geneAnnoData %>%
filter(Anno2 == \P-S\) %>%
group_by(gene) %>%
summarise(
farRight = if (any(distance > 0)) max(distance[distance > 0]) else 0, # Return 0 if no positive distance
farLeft = if (any(distance < 0)) min(distance[distance < 0]) else 0 # Return 0 if no negative distance
) %>%
ungroup()
geneAnnoData <- geneAnnoData %>% left_join(result, by = c(\gene\))
geneAnnoData <- geneAnnoData %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\)) %>%
dplyr::mutate(group = case_when(is.na(farRight) ~ \No\,
(distance > farLeft) & (distance < farRight) ~ \Within\,
TRUE ~ \Outside\),
size = start2 - start1)
data <- geneAnnoData %>% dplyr::select(group, id, size) %>% distinct()
anchor.data <- geneAnnoData %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()
temp1 <- anchor.data %>% dplyr::filter(group == \No\) %>% dplyr::select(-group)
fwrite(temp1, here(consensusDir, \insulated_domain_ps_no.bedpe\), sep = \\t\, col.names = FALSE)
temp2 <- anchor.data %>% dplyr::filter(group == \Within\) %>% dplyr::select(-group)
fwrite(temp2, here(consensusDir, \insulated_domain_ps_within.bedpe\), sep = \\t\, col.names = FALSE)
temp3 <- anchor.data %>% dplyr::filter(group == \Outside\) %>% dplyr::select(-group)
fwrite(temp3, here(consensusDir, \insulated_domain_ps_outside.bedpe\), sep = \\t\, col.names = FALSE)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$size
distance2 <- (data %>% dplyr::filter(group ==group2) )$size
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
# convPvalue(getPvalWilcox(data, \noBoundary\, \withinBoundary\))
# convPvalue(getPvalWilcox(data, \noBoundary\, \outsideBoundary\))
# convPvalue(getPvalWilcox(data, \outsideBoundary\, \withinBoundary\))
p <- ggplot(data, aes(x = group, y = size, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() + scale_y_continuous(labels = label_kb_mb) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \#5EC962\, \#5EC962\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Loop size\)
fileName <- paste0(\insulationBoundary_size\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
########################
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
data <- geneAnnoData %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()
# convPvalue(getPvalWilcox(data, \noBoundary\, \withinBoundary\))
# convPvalue(getPvalWilcox(data, \noBoundary\, \outsideBoundary\))
# convPvalue(getPvalWilcox(data, \outsideBoundary\, \withinBoundary\))
p <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \#5EC962\, \#5EC962\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Δ loop score\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = -0.2,
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\) +
coord_cartesian(ylim = c(-0.8, 0.5))
fileName <- paste0(\insulationBoundary_delta\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
######################
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
data <- geneAnnoData %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
# convPvalue(getPvalWilcox(data, \noBoundary\, \withinBoundary\))
# convPvalue(getPvalWilcox(data, \noBoundary\, \outsideBoundary\))
# convPvalue(getPvalWilcox(data, \outsideBoundary\, \withinBoundary\))
#
p <- ggplot(data, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \#5EC962\, \#5EC962\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2(fc of obs/exp)\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
coord_cartesian(ylim = c(-2, 2))
fileName <- paste0(\insulationBoundary_obsexp\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#####################
gene.withinBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \withinBoundary\))$gene)
gene.outsideBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \outsideBoundary\))$gene)
gene.noBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \noBoundary\))$gene)
gene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
print(\perc of gene with loop within Boundary\)
sum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100
print(\perc of gene with loop outside Boundary\)
sum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100
print(\perc of gene with loop with no Boundary\)
sum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%
dplyr::mutate(group = case_when(
ensembl_gene_id %in% gene.wBoundary ~ \withinBoundary\,
ensembl_gene_id %in% gene.noBoundary ~ \noBoundary\,
TRUE ~ NA
)) %>%
dplyr::filter(!is.na(group)) %>%
dplyr::mutate(absLog2FC = abs(log2FoldChange))
ggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC
distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
convPvalue(getPvalWilcox(diff.RNA, \withinBoundary\, \noBoundary\))
# Create the CDF plot
p <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +
stat_ecdf(size = 0.4 ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Absolute log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 0.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\log2FC_cdf_insulationBoundary\)
width <- panelSize(2.5)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Checking characterstics of anchors
temp1 <- anchor.data %>% dplyr::filter(group == \noBoundary\) %>% dplyr::select(-group)
anchor.noBoundary <- extractAnchor(temp1)
temp2 <- anchor.data %>% dplyr::filter(group == \withinBoundary\) %>% dplyr::select(-group)
anchor.withinBoundary <- extractAnchor(temp2)
temp3 <- anchor.data %>% dplyr::filter(group == \outsideBoundary\) %>% dplyr::select(-group)
anchor.outsideBoundary <- extractAnchor(temp3)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore
distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
getSumScores <- function(track, anchor) {
# Find overlaps between all anchors and track regions at once
overlaps <- findOverlaps(anchor, track)
# Extract the scores and corresponding anchor indices
anchor_indices <- queryHits(overlaps)
track_scores <- score(track)[subjectHits(overlaps)]
# Use tapply to calculate the median scores for each anchor
median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)
# Initialize a numeric vector to store the median scores for each anchor
all_median_scores <- rep(NA, length(anchor))
# Populate the median scores for the anchors that have overlaps
all_median_scores[as.numeric(names(median_scores))] <- median_scores
return(all_median_scores)
}
plotSumScoresBinary <- function(track, peak, name, anchor1, anchor2, anchor3){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
a <- getSumScores(peakTrack, anchor1)
b <- getSumScores(peakTrack, anchor2)
c <- getSumScores(peakTrack, anchor3)
a.tb <- tibble(loop = \noBoundary\,
sumScore = a)
b.tb <- tibble(loop = \withBoundary\,
sumScore = b)
c.tb <- tibble(loop = \outsideBoundary\,
sumScore = c)
data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()
data$loop <- factor(data$loop, levels = c(\noBoundary\, \withBoundary\, \outsideBoundary\))
p12 <- getPvalWilcox(data, \noBoundary\, \withBoundary\)
p13 <- getPvalWilcox(data, \noBoundary\, \outsideBoundary\)
p23 <- getPvalWilcox(data, \withBoundary\, \outsideBoundary\)
p <- ggplot(data, aes(x = loop, y = sumScore)) +
labs(x = NULL, y = paste0(name, \\nlog(avg peak score per anchor)\)) +
geom_violin(aes(fill = loop), color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.size = 1, outlier.stroke = NA) + theme_classic() +
stat_summary(
aes(group = loop), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) + theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
#coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) +
annotate(
\text\, x = 1, y = quantile(data$sumScore, 0.5),
label = paste0(\p12: \, convPvalue(p12), \\n\,
\p13: \, convPvalue(p13), \\n\,
\p23: \, convPvalue(p23)),
color = \black\, hjust = 0, size = 3
)
#+ scale_y_continuous(trans = \log10\)
fileName <- paste0(\ChIP_peak_avgPeakScore_boundaryAnchor_\, name)
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# Sum peak score
####
track <- import(here(refDir, \33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
plotSumScoresBinary(track, peak, \H3K4me3\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
track <- import(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
#plotSumScores(track, peak, \H3K27ac\)
plotSumScoresBinary(track, peak, \H3K27ac\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
track <- import(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.bed\))
#plotSumScores(track, peak, \H3K14ac\)
plotSumScoresBinary(track, peak, \H3K14ac\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
track <- import(here(refDir, \33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \CTCF\)
plotSumScoresBinary(track, peak, \CTCF\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
track <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \RAD21\)
plotSumScoresBinary(track, peak, \RAD21\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
######## LOLA
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
overlaps <- findOverlaps(anchor.noBoundary, atac.gr)
anchor.noBoundary <- pintersect(anchor.noBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
overlaps <- findOverlaps(anchor.withinBoundary, atac.gr)
anchor.withinBoundary <- pintersect(anchor.withinBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
overlaps <- findOverlaps(anchor.outsideBoundary, atac.gr)
anchor.outsideBoundary <- pintersect(anchor.outsideBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchors <- list(anchor.noBoundary, anchor.withBoundary, anchor.outsideBoundary)
tbs <- list()
temps <- list()
alpha <- 0.05
# Process clusters c1 to c8
for (i in 1:3) {
anchor <- anchors[[i]]
# Run LOLA
result <- runLOLA(anchor, anchor.all, lolaDB)
tb <- as_tibble(result)
# Filter and summarize
tb <- tb %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
# Store tb
tbs[[i]] <- tb
# Select and rename oddsRatio
temp <- tb %>% dplyr::select(target, oddsRatio)
colnames(temp) <- c(\target\, paste0(\OR_c\, i))
# Store temp
temps[[i]] <- temp
}
# Merge all temp tables into one
temp <- Reduce(function(x, y) full_join(x, y, by = \target\), temps) %>%
mutate_all(~replace_na(., 1))
colnames(temp) <- c(\target\, \noBoundary\, \withinBoundary\, \outsideBoundary\)
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\\insulationBoundary_delta\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n  \n  \n ###################### \n    \n    minValue <- -4\n  obsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\ndata <- geneAnnoData %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n# \n\np <- ggplot(data, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n    \nfileName <- paste0(\\insulationBoundary_obsexp\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\n\ngene.withinBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\withinBoundary\\))$gene)\ngene.outsideBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\outsideBoundary\\))$gene)\ngene.noBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\noBoundary\\))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nprint(\\perc of gene with loop within Boundary\\)\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop outside Boundary\\)\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop with no Boundary\\)\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \\withinBoundary\\,\n    ensembl_gene_id %in% gene.noBoundary ~ \\noBoundary\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\n\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\nconvPvalue(getPvalWilcox(diff.RNA, \\withinBoundary\\, \\noBoundary\\))\n\n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\nstat_ecdf(size = 0.4 ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 0.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\\log2FC_cdf_insulationBoundary\\)\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Checking characterstics of anchors\ntemp1 <- anchor.data %>% dplyr::filter(group == \\noBoundary\\) %>% dplyr::select(-group)\nanchor.noBoundary <- extractAnchor(temp1)\n\ntemp2 <- anchor.data %>% dplyr::filter(group == \\withinBoundary\\) %>% dplyr::select(-group)\nanchor.withinBoundary <- extractAnchor(temp2)\n\ntemp3 <- anchor.data %>% dplyr::filter(group == \\outsideBoundary\\) %>% dplyr::select(-group)\nanchor.outsideBoundary <- extractAnchor(temp3)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore\n  distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScoresBinary <- function(track, peak, name, anchor1, anchor2, anchor3){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor1)\n  b <- getSumScores(peakTrack, anchor2)\n  c <- getSumScores(peakTrack, anchor3)\n  a.tb <- tibble(loop = \\noBoundary\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\withBoundary\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\outsideBoundary\\,\n                 sumScore = c)\n  \n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\noBoundary\\, \\withBoundary\\, \\outsideBoundary\\))\n  \n  p12 <- getPvalWilcox(data, \\noBoundary\\, \\withBoundary\\)\n  p13 <- getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\)\n  p23 <- getPvalWilcox(data, \\withBoundary\\, \\outsideBoundary\\)\n  p <-  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\\\nlog(avg peak score per anchor)\\)) +\n    geom_violin(aes(fill = loop), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    #coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) + \n    annotate(\n      \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n      label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                     \\p13: \\, convPvalue(p13), \\\\n\\,\n                     \\p23: \\, convPvalue(p23)),\n      color = \\black\\, hjust = 0, size = 3\n    ) \n  #+ scale_y_continuous(trans = \\log10\\)\n  \n  fileName <- paste0(\\ChIP_peak_avgPeakScore_boundaryAnchor_\\, name)\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScoresBinary(track, peak, \\H3K4me3\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n#plotSumScores(track, peak, \\H3K27ac\\)\nplotSumScoresBinary(track, peak, \\H3K27ac\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\n#plotSumScores(track, peak, \\H3K14ac\\)\nplotSumScoresBinary(track, peak, \\H3K14ac\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\CTCF\\)\nplotSumScoresBinary(track, peak, \\CTCF\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\RAD21\\)\nplotSumScoresBinary(track, peak, \\RAD21\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\n\n\n######## LOLA\natac <- fread(here(refDir, \\GSM3106257_ATAC_ESC_1.bed\\)) %>% dplyr::select(V1, V2, V3)\ncolnames(atac) <- c(\\chr\\, \\start\\, \\end\\)\natac.gr <- makeGRangesFromDataFrame(atac)\n\n# LOADING LOOPS\nloop.all <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\noverlaps <- findOverlaps(anchor.noBoundary, atac.gr)\nanchor.noBoundary <- pintersect(anchor.noBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.withinBoundary, atac.gr)\nanchor.withinBoundary <- pintersect(anchor.withinBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.outsideBoundary, atac.gr)\nanchor.outsideBoundary <- pintersect(anchor.outsideBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\n\nanchors <- list(anchor.noBoundary, anchor.withBoundary, anchor.outsideBoundary)\ntbs <- list()\ntemps <- list()\n\nalpha <- 0.05\n\n# Process clusters c1 to c8\nfor (i in 1:3) {\n  \n  anchor <- anchors[[i]]\n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ncolnames(temp) <- c(\\target\\, \\noBoundary\\, \\withinBoundary\\, \\outsideBoundary\\)\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n```"} -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>%\n  dplyr::select(c(1, 2, 3, 4, 5, 6, 7, 11, 12, 24, 29, 31)) %>% unnest(gene)\n\n\ngene.TSS.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V6, TSS)\ncolnames(gene.TSS.tb) <- c(\\ensembl\\, \\TSS\\)\n\n\ngeneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.TSS.tb, by = c(\\gene\\ = \\ensembl\\))\n\ngeneAnnoData <- geneAnnoData %>% \n  dplyr::mutate(distance1 = (start1 + end1)/2 - TSS,\n                distance2 = (start2 + end2)/2 - TSS,\n                distance = if_else(abs(distance1) > abs(distance2), distance1, distance2))\n\nresult <- geneAnnoData %>%\n  filter(Anno2 == \\P-S\\) %>%\n  group_by(gene) %>%\n  summarise(\n    farRight = if (any(distance > 0)) max(distance[distance > 0]) else 0,  # Return 0 if no positive distance\n    farLeft = if (any(distance < 0)) min(distance[distance < 0]) else 0  # Return 0 if no negative distance\n  ) %>%\n  ungroup()\n\ngeneAnnoData <- geneAnnoData %>% left_join(result, by = c(\\gene\\))\n\ngeneAnnoData <- geneAnnoData %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\)) %>%\n  dplyr::mutate(group = case_when(is.na(farRight) ~ \\No\\,\n                                  (distance > farLeft) & (distance < farRight) ~ \\Within\\,\n                                  TRUE ~ \\Outside\\),\n                size = start2 - start1)\n\ndata <- geneAnnoData %>% dplyr::select(group, id, size) %>% distinct()\nanchor.data <- geneAnnoData %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()\n\ntemp1 <- anchor.data %>% dplyr::filter(group == \\No\\) %>% dplyr::select(-group)\nfwrite(temp1, here(consensusDir, \\insulated_domain_ps_no.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp2 <- anchor.data %>% dplyr::filter(group == \\Within\\) %>% dplyr::select(-group)\nfwrite(temp2, here(consensusDir, \\insulated_domain_ps_within.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp3 <- anchor.data %>% dplyr::filter(group == \\Outside\\) %>% dplyr::select(-group)\nfwrite(temp3, here(consensusDir, \\insulated_domain_ps_outside.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$size\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$size\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n\n\np <- ggplot(data, aes(x = group, y = size, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n  stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Loop size\\)\n\nfileName <- paste0(\\insulationBoundary_size\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n  \n########################\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ndata <- geneAnnoData %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n\np <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\\insulationBoundary_delta\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n  \n  \n ###################### \n    \n    minValue <- -4\n  obsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\ndata <- geneAnnoData %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n# \n\np <- ggplot(data, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n    \nfileName <- paste0(\\insulationBoundary_obsexp\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\n\ngene.withinBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\withinBoundary\\))$gene)\ngene.outsideBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\outsideBoundary\\))$gene)\ngene.noBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\noBoundary\\))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nprint(\\perc of gene with loop within Boundary\\)\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop outside Boundary\\)\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop with no Boundary\\)\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \\withinBoundary\\,\n    ensembl_gene_id %in% gene.noBoundary ~ \\noBoundary\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\n\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\nconvPvalue(getPvalWilcox(diff.RNA, \\withinBoundary\\, \\noBoundary\\))\n\n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\nstat_ecdf(size = 0.4 ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 0.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\\log2FC_cdf_insulationBoundary\\)\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Checking characterstics of anchors\ntemp1 <- anchor.data %>% dplyr::filter(group == \\noBoundary\\) %>% dplyr::select(-group)\nanchor.noBoundary <- extractAnchor(temp1)\n\ntemp2 <- anchor.data %>% dplyr::filter(group == \\withinBoundary\\) %>% dplyr::select(-group)\nanchor.withinBoundary <- extractAnchor(temp2)\n\ntemp3 <- anchor.data %>% dplyr::filter(group == \\outsideBoundary\\) %>% dplyr::select(-group)\nanchor.outsideBoundary <- extractAnchor(temp3)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore\n  distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScoresBinary <- function(track, peak, name, anchor1, anchor2, anchor3){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor1)\n  b <- getSumScores(peakTrack, anchor2)\n  c <- getSumScores(peakTrack, anchor3)\n  a.tb <- tibble(loop = \\noBoundary\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\withBoundary\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\outsideBoundary\\,\n                 sumScore = c)\n  \n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\noBoundary\\, \\withBoundary\\, \\outsideBoundary\\))\n  \n  p12 <- getPvalWilcox(data, \\noBoundary\\, \\withBoundary\\)\n  p13 <- getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\)\n  p23 <- getPvalWilcox(data, \\withBoundary\\, \\outsideBoundary\\)\n  p <-  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\\\nlog(avg peak score per anchor)\\)) +\n    geom_violin(aes(fill = loop), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    #coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) + \n    annotate(\n      \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n      label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                     \\p13: \\, convPvalue(p13), \\\\n\\,\n                     \\p23: \\, convPvalue(p23)),\n      color = \\black\\, hjust = 0, size = 3\n    ) \n  #+ scale_y_continuous(trans = \\log10\\)\n  \n  fileName <- paste0(\\ChIP_peak_avgPeakScore_boundaryAnchor_\\, name)\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScoresBinary(track, peak, \\H3K4me3\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n#plotSumScores(track, peak, \\H3K27ac\\)\nplotSumScoresBinary(track, peak, \\H3K27ac\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\n#plotSumScores(track, peak, \\H3K14ac\\)\nplotSumScoresBinary(track, peak, \\H3K14ac\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\CTCF\\)\nplotSumScoresBinary(track, peak, \\CTCF\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\RAD21\\)\nplotSumScoresBinary(track, peak, \\RAD21\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\n\n\n######## LOLA\natac <- fread(here(refDir, \\GSM3106257_ATAC_ESC_1.bed\\)) %>% dplyr::select(V1, V2, V3)\ncolnames(atac) <- c(\\chr\\, \\start\\, \\end\\)\natac.gr <- makeGRangesFromDataFrame(atac)\n\n# LOADING LOOPS\nloop.all <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\noverlaps <- findOverlaps(anchor.noBoundary, atac.gr)\nanchor.noBoundary <- pintersect(anchor.noBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.withinBoundary, atac.gr)\nanchor.withinBoundary <- pintersect(anchor.withinBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.outsideBoundary, atac.gr)\nanchor.outsideBoundary <- pintersect(anchor.outsideBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\n\nanchors <- list(anchor.noBoundary, anchor.withBoundary, anchor.outsideBoundary)\ntbs <- list()\ntemps <- list()\n\nalpha <- 0.05\n\n# Process clusters c1 to c8\nfor (i in 1:3) {\n  \n  anchor <- anchors[[i]]\n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ncolnames(temp) <- c(\\target\\, \\noBoundary\\, \\withinBoundary\\, \\outsideBoundary\\)\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>%\n  dplyr::select(c(1, 2, 3, 4, 5, 6, 7, 11, 12, 24, 29, 31)) %>% unnest(gene)\n\n\ngene.TSS.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3)) %>%\n  dplyr::select(V6, TSS)\ncolnames(gene.TSS.tb) <- c(\\ensembl\\, \\TSS\\)\n\n\ngeneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.TSS.tb, by = c(\\gene\\ = \\ensembl\\))\n\ngeneAnnoData <- geneAnnoData %>% \n  dplyr::mutate(distance1 = (start1 + end1)/2 - TSS,\n                distance2 = (start2 + end2)/2 - TSS,\n                distance = if_else(abs(distance1) > abs(distance2), distance1, distance2))\n\nresult <- geneAnnoData %>%\n  filter(Anno2 == \\P-S\\) %>%\n  group_by(gene) %>%\n  summarise(\n    farRight = if (any(distance > 0)) max(distance[distance > 0]) else 0,  # Return 0 if no positive distance\n    farLeft = if (any(distance < 0)) min(distance[distance < 0]) else 0  # Return 0 if no negative distance\n  ) %>%\n  ungroup()\n\ngeneAnnoData <- geneAnnoData %>% left_join(result, by = c(\\gene\\))\n\ngeneAnnoData <- geneAnnoData %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\)) %>%\n  dplyr::mutate(group = case_when(is.na(farRight) ~ \\No\\,\n                                  (distance > farLeft) & (distance < farRight) ~ \\Within\\,\n                                  TRUE ~ \\Outside\\),\n                size = start2 - start1)\n\ndata <- geneAnnoData %>% dplyr::select(group, id, size) %>% distinct()\nanchor.data <- geneAnnoData %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()\n\ntemp1 <- anchor.data %>% dplyr::filter(group == \\No\\) %>% dplyr::select(-group)\nfwrite(temp1, here(consensusDir, \\insulated_domain_ps_no.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp2 <- anchor.data %>% dplyr::filter(group == \\Within\\) %>% dplyr::select(-group)\nfwrite(temp2, here(consensusDir, \\insulated_domain_ps_within.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp3 <- anchor.data %>% dplyr::filter(group == \\Outside\\) %>% dplyr::select(-group)\nfwrite(temp3, here(consensusDir, \\insulated_domain_ps_outside.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$size\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$size\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n\n\np <- ggplot(data, aes(x = group, y = size, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n  stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n    ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Loop size\\)\n\nfileName <- paste0(\\insulationBoundary_size\\)\nwidth <- panelSize(1.5)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n  \n########################\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ndata <- geneAnnoData %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n\np <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\\insulationBoundary_delta\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n  \n  \n ###################### \n    \n    minValue <- -4\n  obsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ngeneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\\id\\))\n\ndata <- geneAnnoData %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\withinBoundary\\))\n# convPvalue(getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\))\n# convPvalue(getPvalWilcox(data, \\outsideBoundary\\, \\withinBoundary\\))\n# \n\np <- ggplot(data, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\#5EC962\\, \\#5EC962\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n    \nfileName <- paste0(\\insulationBoundary_obsexp\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\n\ngene.withinBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\withinBoundary\\))$gene)\ngene.outsideBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\outsideBoundary\\))$gene)\ngene.noBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \\noBoundary\\))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nprint(\\perc of gene with loop within Boundary\\)\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop outside Boundary\\)\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop with no Boundary\\)\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \\withinBoundary\\,\n    ensembl_gene_id %in% gene.noBoundary ~ \\noBoundary\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\n\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\nconvPvalue(getPvalWilcox(diff.RNA, \\withinBoundary\\, \\noBoundary\\))\n\n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\nstat_ecdf(size = 0.4 ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 0.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\\log2FC_cdf_insulationBoundary\\)\nwidth <- panelSize(2.5)*mmToInch\nheight <- panelSize(1.5)*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\n\n\n### Checking characterstics of anchors\ntemp1 <- anchor.data %>% dplyr::filter(group == \\noBoundary\\) %>% dplyr::select(-group)\nanchor.noBoundary <- extractAnchor(temp1)\n\ntemp2 <- anchor.data %>% dplyr::filter(group == \\withinBoundary\\) %>% dplyr::select(-group)\nanchor.withinBoundary <- extractAnchor(temp2)\n\ntemp3 <- anchor.data %>% dplyr::filter(group == \\outsideBoundary\\) %>% dplyr::select(-group)\nanchor.outsideBoundary <- extractAnchor(temp3)\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore\n  distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ngetSumScores <- function(track, anchor) {\n  # Find overlaps between all anchors and track regions at once\n  overlaps <- findOverlaps(anchor, track)\n  \n  # Extract the scores and corresponding anchor indices\n  anchor_indices <- queryHits(overlaps)\n  track_scores <- score(track)[subjectHits(overlaps)]\n  \n  # Use tapply to calculate the median scores for each anchor\n  median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)\n  \n  # Initialize a numeric vector to store the median scores for each anchor\n  all_median_scores <- rep(NA, length(anchor))\n  \n  # Populate the median scores for the anchors that have overlaps\n  all_median_scores[as.numeric(names(median_scores))] <- median_scores\n  \n  return(all_median_scores)\n}\n\nplotSumScoresBinary <- function(track, peak, name, anchor1, anchor2, anchor3){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor1)\n  b <- getSumScores(peakTrack, anchor2)\n  c <- getSumScores(peakTrack, anchor3)\n  a.tb <- tibble(loop = \\noBoundary\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\withBoundary\\,\n                 sumScore = b)\n  c.tb <- tibble(loop = \\outsideBoundary\\,\n                 sumScore = c)\n  \n  data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\noBoundary\\, \\withBoundary\\, \\outsideBoundary\\))\n  \n  p12 <- getPvalWilcox(data, \\noBoundary\\, \\withBoundary\\)\n  p13 <- getPvalWilcox(data, \\noBoundary\\, \\outsideBoundary\\)\n  p23 <- getPvalWilcox(data, \\withBoundary\\, \\outsideBoundary\\)\n  p <-  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\\\nlog(avg peak score per anchor)\\)) +\n    geom_violin(aes(fill = loop), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    #coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) + \n    annotate(\n      \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n      label = paste0(\\p12: \\, convPvalue(p12), \\\\n\\,\n                     \\p13: \\, convPvalue(p13), \\\\n\\,\n                     \\p23: \\, convPvalue(p23)),\n      color = \\black\\, hjust = 0, size = 3\n    ) \n  #+ scale_y_continuous(trans = \\log10\\)\n  \n  fileName <- paste0(\\ChIP_peak_avgPeakScore_boundaryAnchor_\\, name)\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScoresBinary(track, peak, \\H3K4me3\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\\))\n#plotSumScores(track, peak, \\H3K27ac\\)\nplotSumScoresBinary(track, peak, \\H3K27ac\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\GSM2683440_J1_H3K14ac_mm10Lifted.bed\\))\n#plotSumScores(track, peak, \\H3K14ac\\)\nplotSumScoresBinary(track, peak, \\H3K14ac\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\CTCF\\)\nplotSumScoresBinary(track, peak, \\CTCF\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n#plotSumScores(track, peak, \\RAD21\\)\nplotSumScoresBinary(track, peak, \\RAD21\\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)\n\n\n\n######## LOLA\natac <- fread(here(refDir, \\GSM3106257_ATAC_ESC_1.bed\\)) %>% dplyr::select(V1, V2, V3)\ncolnames(atac) <- c(\\chr\\, \\start\\, \\end\\)\natac.gr <- makeGRangesFromDataFrame(atac)\n\n# LOADING LOOPS\nloop.all <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>% dplyr::filter(Anno2 %in% c(\\P-P\\, \\P-E\\, \\E-E\\))\nanchor.all <- (extractAnchor(loop.all))\noverlaps <- findOverlaps(anchor.all, atac.gr)\nanchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\noverlaps <- findOverlaps(anchor.noBoundary, atac.gr)\nanchor.noBoundary <- pintersect(anchor.noBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.withinBoundary, atac.gr)\nanchor.withinBoundary <- pintersect(anchor.withinBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\noverlaps <- findOverlaps(anchor.outsideBoundary, atac.gr)\nanchor.outsideBoundary <- pintersect(anchor.outsideBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])\n\n\n\nanchors <- list(anchor.noBoundary, anchor.withBoundary, anchor.outsideBoundary)\ntbs <- list()\ntemps <- list()\n\nalpha <- 0.05\n\n# Process clusters c1 to c8\nfor (i in 1:3) {\n  \n  anchor <- anchors[[i]]\n  # Run LOLA\n  result <- runLOLA(anchor, anchor.all, lolaDB)\n  tb <- as_tibble(result)\n  \n  # Filter and summarize\n  tb <- tb %>%\n    dplyr::mutate(target = toupper(antibody)) %>%\n    filter(str_to_lower(cellType) == \\embryonic stem cell\\) %>%\n    dplyr::filter(qValue < alpha) %>%\n    dplyr::group_by(target) %>%\n    slice_min(meanRnk, with_ties = FALSE)\n  \n  # Store tb\n  tbs[[i]] <- tb\n  \n  # Select and rename oddsRatio\n  temp <- tb %>% dplyr::select(target, oddsRatio)\n  colnames(temp) <- c(\\target\\, paste0(\\OR_c\\, i))\n  \n  # Store temp\n  temps[[i]] <- temp\n}\n\n# Merge all temp tables into one\ntemp <- Reduce(function(x, y) full_join(x, y, by = \\target\\), temps) %>%\n  mutate_all(~replace_na(., 1))\ncolnames(temp) <- c(\\target\\, \\noBoundary\\, \\withinBoundary\\, \\outsideBoundary\\)\ndata <- temp %>% column_to_rownames(\\target\\) %>% as.matrix()\n\nlibrary(circlize)\ncol_fun <- colorRamp2(c(1, max(data)), c(\\white\\, \\red\\))\n\n\n#fviz_nbclust(data, kmeans, method = \\wss\\)\n\np <- Heatmap(\n  data,\n  name = \\Odds Ratio\\,                   # Name of the heatmap legend\n  cluster_columns = FALSE,            # Remove column dendrogram\n  show_row_dend = FALSE,\n  col = col_fun,\n  border = TRUE\n)\n```\n```"} -->
```r
```r
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\)) %>%
dplyr::select(c(1, 2, 3, 4, 5, 6, 7, 11, 12, 24, 29, 31)) %>% unnest(gene)
gene.TSS.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3)) %>%
dplyr::select(V6, TSS)
colnames(gene.TSS.tb) <- c(\ensembl\, \TSS\)
geneAnnoData <- geneAnnoData %>% dplyr::left_join(gene.TSS.tb, by = c(\gene\ = \ensembl\))
geneAnnoData <- geneAnnoData %>%
dplyr::mutate(distance1 = (start1 + end1)/2 - TSS,
distance2 = (start2 + end2)/2 - TSS,
distance = if_else(abs(distance1) > abs(distance2), distance1, distance2))
result <- geneAnnoData %>%
filter(Anno2 == \P-S\) %>%
group_by(gene) %>%
summarise(
farRight = if (any(distance > 0)) max(distance[distance > 0]) else 0, # Return 0 if no positive distance
farLeft = if (any(distance < 0)) min(distance[distance < 0]) else 0 # Return 0 if no negative distance
) %>%
ungroup()
geneAnnoData <- geneAnnoData %>% left_join(result, by = c(\gene\))
geneAnnoData <- geneAnnoData %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\)) %>%
dplyr::mutate(group = case_when(is.na(farRight) ~ \No\,
(distance > farLeft) & (distance < farRight) ~ \Within\,
TRUE ~ \Outside\),
size = start2 - start1)
data <- geneAnnoData %>% dplyr::select(group, id, size) %>% distinct()
anchor.data <- geneAnnoData %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()
temp1 <- anchor.data %>% dplyr::filter(group == \No\) %>% dplyr::select(-group)
fwrite(temp1, here(consensusDir, \insulated_domain_ps_no.bedpe\), sep = \\t\, col.names = FALSE)
temp2 <- anchor.data %>% dplyr::filter(group == \Within\) %>% dplyr::select(-group)
fwrite(temp2, here(consensusDir, \insulated_domain_ps_within.bedpe\), sep = \\t\, col.names = FALSE)
temp3 <- anchor.data %>% dplyr::filter(group == \Outside\) %>% dplyr::select(-group)
fwrite(temp3, here(consensusDir, \insulated_domain_ps_outside.bedpe\), sep = \\t\, col.names = FALSE)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$size
distance2 <- (data %>% dplyr::filter(group ==group2) )$size
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
# convPvalue(getPvalWilcox(data, \noBoundary\, \withinBoundary\))
# convPvalue(getPvalWilcox(data, \noBoundary\, \outsideBoundary\))
# convPvalue(getPvalWilcox(data, \outsideBoundary\, \withinBoundary\))
p <- ggplot(data, aes(x = group, y = size, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() + scale_y_continuous(labels = label_kb_mb) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \#5EC962\, \#5EC962\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Loop size\)
fileName <- paste0(\insulationBoundary_size\)
width <- panelSize(1.5)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
########################
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
data <- geneAnnoData %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()
# convPvalue(getPvalWilcox(data, \noBoundary\, \withinBoundary\))
# convPvalue(getPvalWilcox(data, \noBoundary\, \outsideBoundary\))
# convPvalue(getPvalWilcox(data, \outsideBoundary\, \withinBoundary\))
p <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \#5EC962\, \#5EC962\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Δ loop score\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = -0.2,
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\) +
coord_cartesian(ylim = c(-0.8, 0.5))
fileName <- paste0(\insulationBoundary_delta\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
######################
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
geneAnnoData <- geneAnnoData %>% left_join(obsexp, by = c(\id\))
data <- geneAnnoData %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
# convPvalue(getPvalWilcox(data, \noBoundary\, \withinBoundary\))
# convPvalue(getPvalWilcox(data, \noBoundary\, \outsideBoundary\))
# convPvalue(getPvalWilcox(data, \outsideBoundary\, \withinBoundary\))
#
p <- ggplot(data, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \#5EC962\, \#5EC962\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2(fc of obs/exp)\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
coord_cartesian(ylim = c(-2, 2))
fileName <- paste0(\insulationBoundary_obsexp\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#####################
gene.withinBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \withinBoundary\))$gene)
gene.outsideBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \outsideBoundary\))$gene)
gene.noBoundary <- unique((geneAnnoData %>% dplyr::filter(group == \noBoundary\))$gene)
gene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
print(\perc of gene with loop within Boundary\)
sum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100
print(\perc of gene with loop outside Boundary\)
sum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100
print(\perc of gene with loop with no Boundary\)
sum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%
dplyr::mutate(group = case_when(
ensembl_gene_id %in% gene.wBoundary ~ \withinBoundary\,
ensembl_gene_id %in% gene.noBoundary ~ \noBoundary\,
TRUE ~ NA
)) %>%
dplyr::filter(!is.na(group)) %>%
dplyr::mutate(absLog2FC = abs(log2FoldChange))
ggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC
distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
convPvalue(getPvalWilcox(diff.RNA, \withinBoundary\, \noBoundary\))
# Create the CDF plot
p <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +
stat_ecdf(size = 0.4 ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Absolute log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 0.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\log2FC_cdf_insulationBoundary\)
width <- panelSize(2.5)*mmToInch
height <- panelSize(1.5)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
### Checking characterstics of anchors
temp1 <- anchor.data %>% dplyr::filter(group == \noBoundary\) %>% dplyr::select(-group)
anchor.noBoundary <- extractAnchor(temp1)
temp2 <- anchor.data %>% dplyr::filter(group == \withinBoundary\) %>% dplyr::select(-group)
anchor.withinBoundary <- extractAnchor(temp2)
temp3 <- anchor.data %>% dplyr::filter(group == \outsideBoundary\) %>% dplyr::select(-group)
anchor.outsideBoundary <- extractAnchor(temp3)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(loop ==group1) )$sumScore
distance2 <- (data %>% dplyr::filter(loop ==group2) )$sumScore
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
getSumScores <- function(track, anchor) {
# Find overlaps between all anchors and track regions at once
overlaps <- findOverlaps(anchor, track)
# Extract the scores and corresponding anchor indices
anchor_indices <- queryHits(overlaps)
track_scores <- score(track)[subjectHits(overlaps)]
# Use tapply to calculate the median scores for each anchor
median_scores <- tapply(track_scores, anchor_indices, mean, na.rm = TRUE)
# Initialize a numeric vector to store the median scores for each anchor
all_median_scores <- rep(NA, length(anchor))
# Populate the median scores for the anchors that have overlaps
all_median_scores[as.numeric(names(median_scores))] <- median_scores
return(all_median_scores)
}
plotSumScoresBinary <- function(track, peak, name, anchor1, anchor2, anchor3){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
a <- getSumScores(peakTrack, anchor1)
b <- getSumScores(peakTrack, anchor2)
c <- getSumScores(peakTrack, anchor3)
a.tb <- tibble(loop = \noBoundary\,
sumScore = a)
b.tb <- tibble(loop = \withBoundary\,
sumScore = b)
c.tb <- tibble(loop = \outsideBoundary\,
sumScore = c)
data <- bind_rows(a.tb, b.tb, c.tb) %>% drop_na()
data$loop <- factor(data$loop, levels = c(\noBoundary\, \withBoundary\, \outsideBoundary\))
p12 <- getPvalWilcox(data, \noBoundary\, \withBoundary\)
p13 <- getPvalWilcox(data, \noBoundary\, \outsideBoundary\)
p23 <- getPvalWilcox(data, \withBoundary\, \outsideBoundary\)
p <- ggplot(data, aes(x = loop, y = sumScore)) +
labs(x = NULL, y = paste0(name, \\nlog(avg peak score per anchor)\)) +
geom_violin(aes(fill = loop), color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.size = 1, outlier.stroke = NA) + theme_classic() +
stat_summary(
aes(group = loop), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) + theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
#coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) +
annotate(
\text\, x = 1, y = quantile(data$sumScore, 0.5),
label = paste0(\p12: \, convPvalue(p12), \\n\,
\p13: \, convPvalue(p13), \\n\,
\p23: \, convPvalue(p23)),
color = \black\, hjust = 0, size = 3
)
#+ scale_y_continuous(trans = \log10\)
fileName <- paste0(\ChIP_peak_avgPeakScore_boundaryAnchor_\, name)
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# Sum peak score
####
track <- import(here(refDir, \33255_H3K4me3_04-745_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33255_H3K4me3_04-745_Bruce-4_peaks.mergePeak.bed\))
plotSumScoresBinary(track, peak, \H3K4me3\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
track <- import(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2438476_EC-DG-3458-H3K27AC_ASYN_1.narrowPeak.bed\))
#plotSumScores(track, peak, \H3K27ac\)
plotSumScoresBinary(track, peak, \H3K27ac\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
track <- import(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.black.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \GSM2683440_J1_H3K14ac_mm10Lifted.bed\))
#plotSumScores(track, peak, \H3K14ac\)
plotSumScoresBinary(track, peak, \H3K14ac\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
track <- import(here(refDir, \33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \CTCF\)
plotSumScoresBinary(track, peak, \CTCF\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
track <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
#plotSumScores(track, peak, \RAD21\)
plotSumScoresBinary(track, peak, \RAD21\, anchor.noBoundary, anchor.withinBoundary, anchor.outsideBoundary)
######## LOLA
atac <- fread(here(refDir, \GSM3106257_ATAC_ESC_1.bed\)) %>% dplyr::select(V1, V2, V3)
colnames(atac) <- c(\chr\, \start\, \end\)
atac.gr <- makeGRangesFromDataFrame(atac)
# LOADING LOOPS
loop.all <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\, \E-E\))
anchor.all <- (extractAnchor(loop.all))
overlaps <- findOverlaps(anchor.all, atac.gr)
anchor.all <- pintersect(anchor.all[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
overlaps <- findOverlaps(anchor.noBoundary, atac.gr)
anchor.noBoundary <- pintersect(anchor.noBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
overlaps <- findOverlaps(anchor.withinBoundary, atac.gr)
anchor.withinBoundary <- pintersect(anchor.withinBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
overlaps <- findOverlaps(anchor.outsideBoundary, atac.gr)
anchor.outsideBoundary <- pintersect(anchor.outsideBoundary[queryHits(overlaps)], atac.gr[subjectHits(overlaps)])
anchors <- list(anchor.noBoundary, anchor.withBoundary, anchor.outsideBoundary)
tbs <- list()
temps <- list()
alpha <- 0.05
# Process clusters c1 to c8
for (i in 1:3) {
anchor <- anchors[[i]]
# Run LOLA
result <- runLOLA(anchor, anchor.all, lolaDB)
tb <- as_tibble(result)
# Filter and summarize
tb <- tb %>%
dplyr::mutate(target = toupper(antibody)) %>%
filter(str_to_lower(cellType) == \embryonic stem cell\) %>%
dplyr::filter(qValue < alpha) %>%
dplyr::group_by(target) %>%
slice_min(meanRnk, with_ties = FALSE)
# Store tb
tbs[[i]] <- tb
# Select and rename oddsRatio
temp <- tb %>% dplyr::select(target, oddsRatio)
colnames(temp) <- c(\target\, paste0(\OR_c\, i))
# Store temp
temps[[i]] <- temp
}
# Merge all temp tables into one
temp <- Reduce(function(x, y) full_join(x, y, by = \target\), temps) %>%
mutate_all(~replace_na(., 1))
colnames(temp) <- c(\target\, \noBoundary\, \withinBoundary\, \outsideBoundary\)
data <- temp %>% column_to_rownames(\target\) %>% as.matrix()
library(circlize)
col_fun <- colorRamp2(c(1, max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
```
```
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### [2.35] Checking CTCF motif presence at gene TSS
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-->\n\n```r\ntss.5kb.group1 <- fread(here(refDir, \\TSS_binaryGroup1.bed\\)) %>%\n  dplyr::mutate(start = (V2 + V3)/2 - 2500,\n                end = (V2 + V3)/2 + 2500,\n                chr = V1) %>%\n  dplyr::select(chr, start, end)\ntss.5kb.group1 <- makeGRangesFromDataFrame(tss.5kb.group1)\n\ntss.5kb.group2 <- fread(here(refDir, \\TSS_binaryGroup2.bed\\)) %>%\n  dplyr::mutate(start = (V2 + V3)/2 - 2500,\n                end = (V2 + V3)/2 + 2500,\n                chr = V1) %>%\n  dplyr::select(chr, start, end)\ntss.5kb.group2 <- makeGRangesFromDataFrame(tss.5kb.group2)\n\n\nplotSumScoresBinary <- function(track, peak, name, anchor1, anchor2){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor1)\n  b <- getSumScores(peakTrack, anchor2)\n  a.tb <- tibble(loop = \\group1\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\group2\\,\n                 sumScore = b)\n  \n  data <- bind_rows(a.tb, b.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\group1\\, \\group2\\))\n  \n  p12 <- getPvalWilcox(data, \\group1\\, \\group2\\)\n  p <-  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\\\nlog(avg peak score per anchor)\\)) +\n    geom_violin(aes(fill = loop), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    #coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) + \n    annotate(\n      \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) \n  #+ scale_y_continuous(trans = \\log10\\)\n  \n  fileName <- paste0(\\ChIP_peak_avgPeakScore_binaryGroup_\\, name)\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScoresBinary(track, peak, \\CTCF\\, tss.5kb.group1, tss.5kb.group2)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScoresBinary(track, peak, \\RAD21\\, tss.5kb.group1, tss.5kb.group2)\n\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
tss.5kb.group1 <- fread(here(refDir, \TSS_binaryGroup1.bed\)) %>%
dplyr::mutate(start = (V2 + V3)/2 - 2500,
end = (V2 + V3)/2 + 2500,
chr = V1) %>%
dplyr::select(chr, start, end)
tss.5kb.group1 <- makeGRangesFromDataFrame(tss.5kb.group1)
tss.5kb.group2 <- fread(here(refDir, \TSS_binaryGroup2.bed\)) %>%
dplyr::mutate(start = (V2 + V3)/2 - 2500,
end = (V2 + V3)/2 + 2500,
chr = V1) %>%
dplyr::select(chr, start, end)
tss.5kb.group2 <- makeGRangesFromDataFrame(tss.5kb.group2)
plotSumScoresBinary <- function(track, peak, name, anchor1, anchor2){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
a <- getSumScores(peakTrack, anchor1)
b <- getSumScores(peakTrack, anchor2)
a.tb <- tibble(loop = \group1\,
sumScore = a)
b.tb <- tibble(loop = \group2\,
sumScore = b)
data <- bind_rows(a.tb, b.tb) %>% drop_na()
data$loop <- factor(data$loop, levels = c(\group1\, \group2\))
p12 <- getPvalWilcox(data, \group1\, \group2\)
p <- ggplot(data, aes(x = loop, y = sumScore)) +
labs(x = NULL, y = paste0(name, \\nlog(avg peak score per anchor)\)) +
geom_violin(aes(fill = loop), color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.size = 1, outlier.stroke = NA) + theme_classic() +
stat_summary(
aes(group = loop), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) + theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
#coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) +
annotate(
\text\, x = 1, y = quantile(data$sumScore, 0.5),
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
)
#+ scale_y_continuous(trans = \log10\)
fileName <- paste0(\ChIP_peak_avgPeakScore_binaryGroup_\, name)
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# Sum peak score
####
track <- import(here(refDir, \33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
plotSumScoresBinary(track, peak, \CTCF\, tss.5kb.group1, tss.5kb.group2)
track <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
plotSumScoresBinary(track, peak, \RAD21\, tss.5kb.group1, tss.5kb.group2)
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-->\n\n```r\n```r\ntss.5kb.group1 <- fread(here(refDir, \\TSS_binaryGroup1.bed\\)) %>%\n  dplyr::mutate(start = (V2 + V3)/2 - 2500,\n                end = (V2 + V3)/2 + 2500,\n                chr = V1) %>%\n  dplyr::select(chr, start, end)\ntss.5kb.group1 <- makeGRangesFromDataFrame(tss.5kb.group1)\n\ntss.5kb.group2 <- fread(here(refDir, \\TSS_binaryGroup2.bed\\)) %>%\n  dplyr::mutate(start = (V2 + V3)/2 - 2500,\n                end = (V2 + V3)/2 + 2500,\n                chr = V1) %>%\n  dplyr::select(chr, start, end)\ntss.5kb.group2 <- makeGRangesFromDataFrame(tss.5kb.group2)\n\n\nplotSumScoresBinary <- function(track, peak, name, anchor1, anchor2){\n  peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]\n  a <- getSumScores(peakTrack, anchor1)\n  b <- getSumScores(peakTrack, anchor2)\n  a.tb <- tibble(loop = \\group1\\,\n                 sumScore = a)\n  b.tb <- tibble(loop = \\group2\\,\n                 sumScore = b)\n  \n  data <- bind_rows(a.tb, b.tb) %>% drop_na()\n  data$loop <- factor(data$loop, levels = c(\\group1\\, \\group2\\))\n  \n  p12 <- getPvalWilcox(data, \\group1\\, \\group2\\)\n  p <-  ggplot(data, aes(x = loop, y = sumScore)) + \n    labs(x = NULL, y = paste0(name, \\\\nlog(avg peak score per anchor)\\)) +\n    geom_violin(aes(fill = loop), color = \\black\\,\n                linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                show.legend = FALSE) +\n    geom_boxplot(width = 0.1, color = \\black\\,\n                 linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n                 outlier.size = 1, outlier.stroke = NA) + theme_classic() +\n    stat_summary(\n      aes(group = loop), fun = mean,\n      geom = \\point\\, shape = 21, size = 1,\n      fill = \\red\\, color = \\black\\\n    ) + theme(\n      axis.title = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text = element_text(\n        size = fontSizeS, family = fontType, color = \\#000000\\\n      ),\n      axis.text.x = element_text(\n        angle = 45, hjust = 1, vjust = 1\n      ),\n      axis.line = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      axis.ticks = element_line(\n        color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n      ),\n      panel.background = element_rect(fill = \\transparent\\),\n      legend.text = element_text(family = fontType, size = fontSizeS),\n      legend.title = element_text(family = fontType, size = fontSizeS)\n    ) + \n    #coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) + \n    annotate(\n      \\text\\, x = 1, y = quantile(data$sumScore, 0.5),\n      label = paste0(\\p12: \\, convPvalue(p12)),\n      color = \\black\\, hjust = 0, size = 3\n    ) \n  #+ scale_y_continuous(trans = \\log10\\)\n  \n  fileName <- paste0(\\ChIP_peak_avgPeakScore_binaryGroup_\\, name)\n  width <- panelSize(2)*mmToInch\n  height <- panelSize(2)*mmToInch\n  png(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\n  print(p)\n  dev.off()\n  svglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\n  print(p)\n  dev.off()\n}\n\n# Sum peak score\n####\ntrack <- import(here(refDir, \\33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScoresBinary(track, peak, \\CTCF\\, tss.5kb.group1, tss.5kb.group2)\n\ntrack <- import(here(refDir, \\33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\\), format = \\BigWig\\)\npeak <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\nplotSumScoresBinary(track, peak, \\RAD21\\, tss.5kb.group1, tss.5kb.group2)\n\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
tss.5kb.group1 <- fread(here(refDir, \TSS_binaryGroup1.bed\)) %>%
dplyr::mutate(start = (V2 + V3)/2 - 2500,
end = (V2 + V3)/2 + 2500,
chr = V1) %>%
dplyr::select(chr, start, end)
tss.5kb.group1 <- makeGRangesFromDataFrame(tss.5kb.group1)
tss.5kb.group2 <- fread(here(refDir, \TSS_binaryGroup2.bed\)) %>%
dplyr::mutate(start = (V2 + V3)/2 - 2500,
end = (V2 + V3)/2 + 2500,
chr = V1) %>%
dplyr::select(chr, start, end)
tss.5kb.group2 <- makeGRangesFromDataFrame(tss.5kb.group2)
plotSumScoresBinary <- function(track, peak, name, anchor1, anchor2){
peakTrack <- track[unique(queryHits(findOverlaps(track, peak)))]
a <- getSumScores(peakTrack, anchor1)
b <- getSumScores(peakTrack, anchor2)
a.tb <- tibble(loop = \group1\,
sumScore = a)
b.tb <- tibble(loop = \group2\,
sumScore = b)
data <- bind_rows(a.tb, b.tb) %>% drop_na()
data$loop <- factor(data$loop, levels = c(\group1\, \group2\))
p12 <- getPvalWilcox(data, \group1\, \group2\)
p <- ggplot(data, aes(x = loop, y = sumScore)) +
labs(x = NULL, y = paste0(name, \\nlog(avg peak score per anchor)\)) +
geom_violin(aes(fill = loop), color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.1, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.size = 1, outlier.stroke = NA) + theme_classic() +
stat_summary(
aes(group = loop), fun = mean,
geom = \point\, shape = 21, size = 1,
fill = \red\, color = \black\
) + theme(
axis.title = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
#coord_cartesian(ylim = c(quantile(data$sumScore, 0.0), quantile(data$sumScore, 0.99))) +
annotate(
\text\, x = 1, y = quantile(data$sumScore, 0.5),
label = paste0(\p12: \, convPvalue(p12)),
color = \black\, hjust = 0, size = 3
)
#+ scale_y_continuous(trans = \log10\)
fileName <- paste0(\ChIP_peak_avgPeakScore_binaryGroup_\, name)
width <- panelSize(2)*mmToInch
height <- panelSize(2)*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
}
# Sum peak score
####
track <- import(here(refDir, \33248_CTCF_07-729_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.bed\))
plotSumScoresBinary(track, peak, \CTCF\, tss.5kb.group1, tss.5kb.group2)
track <- import(here(refDir, \33250_RAD21_ab992_Bruce-4_trim_q20_dedup_black_depthNorm.bw\), format = \BigWig\)
peak <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
plotSumScoresBinary(track, peak, \RAD21\, tss.5kb.group1, tss.5kb.group2)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [2.36] Finding farthest P-S
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<!-- rnb-chunk-begin -->
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# Create the data
data <- data.frame(
Group = c(\Group1\, \Group2\),
Value = c(31.03, 10.64)
)
# Create the bar plot
p <- ggplot(data, aes(x = Group, y = Value, fill = Group)) +
geom_bar(stat = \identity\, color = \black\, width = 0.7, alpha = 0.6, show.legend = FALSE,
linewidth = lineThick * mmToLineUnit, lineend = \square\) + # Use identity for raw values
labs(y = \% of TSS on stripe\) +
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\perc_on_stripe_binaryGroup\)
width <- panelSize(1.25)*mmToInch
height <- panelSize(1.2)*mmToInch
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
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```r
```r
# Create the data
data <- data.frame(
Group = c(\Group1\, \Group2\),
Value = c(31.03, 10.64)
)
# Create the bar plot
p <- ggplot(data, aes(x = Group, y = Value, fill = Group)) +
geom_bar(stat = \identity\, color = \black\, width = 0.7, alpha = 0.6, show.legend = FALSE,
linewidth = lineThick * mmToLineUnit, lineend = \square\) + # Use identity for raw values
labs(y = \% of TSS on stripe\) +
theme_classic() +
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\perc_on_stripe_binaryGroup\)
width <- panelSize(1.25)*mmToInch
height <- panelSize(1.2)*mmToInch
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [3.37] Checking RAD21 level at tss
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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temp <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>% dplyr::select(c(1, 2))
geneList <- (temp %>% dplyr::filter(external_gene_name %in% c(\Klf4\, \Tbx3\, \Jun\, \Fosl2\, \Myc\, \Phlda1\)))$ensembl_gene_id
# LOAD ANNOTATED LOOP
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
filteredLoop <- geneAnnoData %>% unnest(gene) %>% dplyr::filter(gene %in% geneList)
fwrite(filteredLoop, here(consensusDir, paste0(\P-N_selectedGene.bedpe\)),
sep = \\t\, col.names = FALSE)
temploop <- filteredLoop %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\))
fwrite(temploop, here(consensusDir, paste0(\P-PE_selectedGene.bedpe\)),
sep = \\t\, col.names = FALSE)
temploop <- filteredLoop %>% dplyr::filter(Anno2 %in% c(\P-S\))
fwrite(temploop, here(consensusDir, paste0(\P-S_selectedGene.bedpe\)),
sep = \\t\, col.names = FALSE)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
temp <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>% dplyr::select(c(1, 2))
geneList <- (temp %>% dplyr::filter(external_gene_name %in% c(\Klf4\, \Tbx3\, \Jun\, \Fosl2\, \Myc\, \Phlda1\)))$ensembl_gene_id
# LOAD ANNOTATED LOOP
name <- \chromo_cons_annoHierarchy\
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1)
filteredLoop <- geneAnnoData %>% unnest(gene) %>% dplyr::filter(gene %in% geneList)
fwrite(filteredLoop, here(consensusDir, paste0(\P-N_selectedGene.bedpe\)),
sep = \\t\, col.names = FALSE)
temploop <- filteredLoop %>% dplyr::filter(Anno2 %in% c(\P-P\, \P-E\))
fwrite(temploop, here(consensusDir, paste0(\P-PE_selectedGene.bedpe\)),
sep = \\t\, col.names = FALSE)
temploop <- filteredLoop %>% dplyr::filter(Anno2 %in% c(\P-S\))
fwrite(temploop, here(consensusDir, paste0(\P-S_selectedGene.bedpe\)),
sep = \\t\, col.names = FALSE)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [3.38] Stripe percentage
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\n# IMPORT S-S loops\nallLoops <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\))\nstrLoops <- allLoops %>% dplyr::filter(Anno2 %in% c(\\S-S\\))\ntemp <- strLoops %>% dplyr::select(c(1, 2, 6))\ncolnames(temp) <- c(\\chrom\\, \\start\\, \\end\\)\nstrLoopsGr <- makeGRangesFromDataFrame(temp)\n\n# IMPORT TSS\nflankSize <- 10\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6, V5)\ncolnames(gene.tb) <- c(\\chrom\\, \\start\\, \\end\\, \\ensembl\\, \\gene\\)\ngeneGr <- makeGRangesFromDataFrame(gene.tb, keep.extra.columns = TRUE)\n\n\n# Find overlaps between loops and TSS\noverlaps <- findOverlaps(geneGr, strLoopsGr)\n\n# Annotate overlaps\ntss_with_loops <- geneGr[queryHits(overlaps)]\nloops_with_tss <- strLoopsGr[subjectHits(overlaps)]\n\n# Combine into a data frame for processing\nloop_data <- data.frame(gene = tss_with_loops$ensembl,\n                        loop_chr = seqnames(loops_with_tss),\n                        loop_start = start(loops_with_tss),\n                        loop_end = end(loops_with_tss),\n                        loop_width = width(loops_with_tss))\n\n# Identify the largest loop for each gene\nlargest_loops <- loop_data[order(loop_data$gene, -loop_data$loop_width), ]\nlargest_loops <- largest_loops[!duplicated(largest_loops$gene), ]\nlargest_loops <- largest_loops %>% dplyr::mutate(loopID = paste(loop_chr, loop_start, loop_end, sep = \\_\\))\nlargest_loops <- largest_loops[order(largest_loops$loop_chr, largest_loops$loop_start),]\nrownames(largest_loops) <- NULL\n\n# Checking smaller loops\nallLoopsGr <- makeGRangesFromDataFrame(allLoops, \n                                       seqnames.field = \\chrom1\\, \n                                       start.field = \\start1\\, \n                                       end.field = \\end2\\)\nlargestLoopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                           seqnames.field = \\loop_chr\\, \n                                           start.field = \\loop_start\\, \n                                           end.field = \\loop_end\\)\nlargestLoopsGr <- sort(unique(largestLoopsGr))\ncomplete_overlaps <- subsetByOverlaps(allLoopsGr, largestLoopsGr, type = \\within\\)\noverlaps <- findOverlaps(complete_overlaps, largestLoopsGr)\n\n\ncompleteOverlapDf <- as.data.frame(complete_overlaps) %>% dplyr::mutate(loopIDAll = paste(seqnames, start, end, sep = \\_\\))\nlargestLoopsDf <- as.data.frame(largestLoopsGr) %>% dplyr::mutate(loopID = paste(seqnames, start, end, sep = \\_\\))\n\nloopIDpairs <- data.frame(loopID = completeOverlapDf$loopIDAll[queryHits(overlaps)],\n                          loopID2 = largestLoopsDf$loopID[subjectHits(overlaps)])\n\nallLoopsAnnotated <- allLoops %>% dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\)) %>% dplyr::left_join(loopIDpairs, by = c(\\loopID\\))\n\ntemp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\\P-E\\, \\P-P\\, \\E-E\\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))\nggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = hasSS), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\PE-PE\\) + geom_hline(yintercept = 0)\n\n\n# Counting loop types\nallLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%\n  dplyr::filter(!(loopID == loopID2))\ntemp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)\nresult <- temp %>%\n  group_by(loopID2, Anno2) %>%\n  summarise(count = n(), .groups = 'drop') %>%\n  group_by(loopID2) %>%\n  mutate(frequency = count / sum(count)) %>%\n  arrange(loopID2, Anno2)\n\nresult <- largest_loops %>% dplyr::left_join(result, by = c(\\loopID\\ = \\loopID2\\))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(result, by = c(\\gene\\))\ndata <- data %>% filter(complete.cases(.))\n\nall_anno2 <- unique(data$Anno2)\n\n# Calculate average frequency of Anno2 for each gene in each group\nresult <- data %>%\n  # Ensure all possible Anno2 values are present for each gene and group\n  complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>% \n  dplyr::mutate(group = case_when(\n    gene %in% gene.group1 ~ \\group1\\,\n    gene %in% gene.group2 ~ \\group2\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::select(group, Anno2, frequency)\n  \n\nggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()\n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
# IMPORT S-S loops
allLoops <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
strLoops <- allLoops %>% dplyr::filter(Anno2 %in% c(\S-S\))
temp <- strLoops %>% dplyr::select(c(1, 2, 6))
colnames(temp) <- c(\chrom\, \start\, \end\)
strLoopsGr <- makeGRangesFromDataFrame(temp)
# IMPORT TSS
flankSize <- 10
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chrom\, \start\, \end\, \ensembl\, \gene\)
geneGr <- makeGRangesFromDataFrame(gene.tb, keep.extra.columns = TRUE)
# Find overlaps between loops and TSS
overlaps <- findOverlaps(geneGr, strLoopsGr)
# Annotate overlaps
tss_with_loops <- geneGr[queryHits(overlaps)]
loops_with_tss <- strLoopsGr[subjectHits(overlaps)]
# Combine into a data frame for processing
loop_data <- data.frame(gene = tss_with_loops$ensembl,
loop_chr = seqnames(loops_with_tss),
loop_start = start(loops_with_tss),
loop_end = end(loops_with_tss),
loop_width = width(loops_with_tss))
# Identify the largest loop for each gene
largest_loops <- loop_data[order(loop_data$gene, -loop_data$loop_width), ]
largest_loops <- largest_loops[!duplicated(largest_loops$gene), ]
largest_loops <- largest_loops %>% dplyr::mutate(loopID = paste(loop_chr, loop_start, loop_end, sep = \_\))
largest_loops <- largest_loops[order(largest_loops$loop_chr, largest_loops$loop_start),]
rownames(largest_loops) <- NULL
# Checking smaller loops
allLoopsGr <- makeGRangesFromDataFrame(allLoops,
seqnames.field = \chrom1\,
start.field = \start1\,
end.field = \end2\)
largestLoopsGr <- makeGRangesFromDataFrame(largest_loops,
seqnames.field = \loop_chr\,
start.field = \loop_start\,
end.field = \loop_end\)
largestLoopsGr <- sort(unique(largestLoopsGr))
complete_overlaps <- subsetByOverlaps(allLoopsGr, largestLoopsGr, type = \within\)
overlaps <- findOverlaps(complete_overlaps, largestLoopsGr)
completeOverlapDf <- as.data.frame(complete_overlaps) %>% dplyr::mutate(loopIDAll = paste(seqnames, start, end, sep = \_\))
largestLoopsDf <- as.data.frame(largestLoopsGr) %>% dplyr::mutate(loopID = paste(seqnames, start, end, sep = \_\))
loopIDpairs <- data.frame(loopID = completeOverlapDf$loopIDAll[queryHits(overlaps)],
loopID2 = largestLoopsDf$loopID[subjectHits(overlaps)])
allLoopsAnnotated <- allLoops %>% dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \_\)) %>% dplyr::left_join(loopIDpairs, by = c(\loopID\))
temp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\P-E\, \P-P\, \E-E\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))
ggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = hasSS), fun = mean,
geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\) +
ggtitle(\PE-PE\) + geom_hline(yintercept = 0)
# Counting loop types
allLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%
dplyr::filter(!(loopID == loopID2))
temp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)
result <- temp %>%
group_by(loopID2, Anno2) %>%
summarise(count = n(), .groups = 'drop') %>%
group_by(loopID2) %>%
mutate(frequency = count / sum(count)) %>%
arrange(loopID2, Anno2)
result <- largest_loops %>% dplyr::left_join(result, by = c(\loopID\ = \loopID2\))
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(result, by = c(\gene\))
data <- data %>% filter(complete.cases(.))
all_anno2 <- unique(data$Anno2)
# Calculate average frequency of Anno2 for each gene in each group
result <- data %>%
# Ensure all possible Anno2 values are present for each gene and group
complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>%
dplyr::mutate(group = case_when(
gene %in% gene.group1 ~ \group1\,
gene %in% gene.group2 ~ \group2\,
TRUE ~ NA
)) %>%
dplyr::select(group, Anno2, frequency)
ggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n\n# IMPORT S-S loops\nallLoops <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\))\nstrLoops <- allLoops %>% dplyr::filter(Anno2 %in% c(\\S-S\\))\ntemp <- strLoops %>% dplyr::select(c(1, 2, 6))\ncolnames(temp) <- c(\\chrom\\, \\start\\, \\end\\)\nstrLoopsGr <- makeGRangesFromDataFrame(temp)\n\n# IMPORT TSS\nflankSize <- 10\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6, V5)\ncolnames(gene.tb) <- c(\\chrom\\, \\start\\, \\end\\, \\ensembl\\, \\gene\\)\ngeneGr <- makeGRangesFromDataFrame(gene.tb, keep.extra.columns = TRUE)\n\n\n# Find overlaps between loops and TSS\noverlaps <- findOverlaps(geneGr, strLoopsGr)\n\n# Annotate overlaps\ntss_with_loops <- geneGr[queryHits(overlaps)]\nloops_with_tss <- strLoopsGr[subjectHits(overlaps)]\n\n# Combine into a data frame for processing\nloop_data <- data.frame(gene = tss_with_loops$ensembl,\n                        loop_chr = seqnames(loops_with_tss),\n                        loop_start = start(loops_with_tss),\n                        loop_end = end(loops_with_tss),\n                        loop_width = width(loops_with_tss))\n\n# Identify the largest loop for each gene\nlargest_loops <- loop_data[order(loop_data$gene, -loop_data$loop_width), ]\nlargest_loops <- largest_loops[!duplicated(largest_loops$gene), ]\nlargest_loops <- largest_loops %>% dplyr::mutate(loopID = paste(loop_chr, loop_start, loop_end, sep = \\_\\))\nlargest_loops <- largest_loops[order(largest_loops$loop_chr, largest_loops$loop_start),]\nrownames(largest_loops) <- NULL\n\n# Checking smaller loops\nallLoopsGr <- makeGRangesFromDataFrame(allLoops, \n                                       seqnames.field = \\chrom1\\, \n                                       start.field = \\start1\\, \n                                       end.field = \\end2\\)\nlargestLoopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                           seqnames.field = \\loop_chr\\, \n                                           start.field = \\loop_start\\, \n                                           end.field = \\loop_end\\)\nlargestLoopsGr <- sort(unique(largestLoopsGr))\ncomplete_overlaps <- subsetByOverlaps(allLoopsGr, largestLoopsGr, type = \\within\\)\noverlaps <- findOverlaps(complete_overlaps, largestLoopsGr)\n\n\ncompleteOverlapDf <- as.data.frame(complete_overlaps) %>% dplyr::mutate(loopIDAll = paste(seqnames, start, end, sep = \\_\\))\nlargestLoopsDf <- as.data.frame(largestLoopsGr) %>% dplyr::mutate(loopID = paste(seqnames, start, end, sep = \\_\\))\n\nloopIDpairs <- data.frame(loopID = completeOverlapDf$loopIDAll[queryHits(overlaps)],\n                          loopID2 = largestLoopsDf$loopID[subjectHits(overlaps)])\n\nallLoopsAnnotated <- allLoops %>% dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\)) %>% dplyr::left_join(loopIDpairs, by = c(\\loopID\\))\n\ntemp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\\P-E\\, \\P-P\\, \\E-E\\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))\nggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = hasSS), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\PE-PE\\) + geom_hline(yintercept = 0)\n\n\n# Counting loop types\nallLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%\n  dplyr::filter(!(loopID == loopID2))\ntemp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)\nresult <- temp %>%\n  group_by(loopID2, Anno2) %>%\n  summarise(count = n(), .groups = 'drop') %>%\n  group_by(loopID2) %>%\n  mutate(frequency = count / sum(count)) %>%\n  arrange(loopID2, Anno2)\n\nresult <- largest_loops %>% dplyr::left_join(result, by = c(\\loopID\\ = \\loopID2\\))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(result, by = c(\\gene\\))\ndata <- data %>% filter(complete.cases(.))\n\nall_anno2 <- unique(data$Anno2)\n\n# Calculate average frequency of Anno2 for each gene in each group\nresult <- data %>%\n  # Ensure all possible Anno2 values are present for each gene and group\n  complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>% \n  dplyr::mutate(group = case_when(\n    gene %in% gene.group1 ~ \\group1\\,\n    gene %in% gene.group2 ~ \\group2\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::select(group, Anno2, frequency)\n  \n\nggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
name <- \chromo_cons_annoHierarchy\
# IMPORT S-S loops
allLoops <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
strLoops <- allLoops %>% dplyr::filter(Anno2 %in% c(\S-S\))
temp <- strLoops %>% dplyr::select(c(1, 2, 6))
colnames(temp) <- c(\chrom\, \start\, \end\)
strLoopsGr <- makeGRangesFromDataFrame(temp)
# IMPORT TSS
flankSize <- 10
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chrom\, \start\, \end\, \ensembl\, \gene\)
geneGr <- makeGRangesFromDataFrame(gene.tb, keep.extra.columns = TRUE)
# Find overlaps between loops and TSS
overlaps <- findOverlaps(geneGr, strLoopsGr)
# Annotate overlaps
tss_with_loops <- geneGr[queryHits(overlaps)]
loops_with_tss <- strLoopsGr[subjectHits(overlaps)]
# Combine into a data frame for processing
loop_data <- data.frame(gene = tss_with_loops$ensembl,
loop_chr = seqnames(loops_with_tss),
loop_start = start(loops_with_tss),
loop_end = end(loops_with_tss),
loop_width = width(loops_with_tss))
# Identify the largest loop for each gene
largest_loops <- loop_data[order(loop_data$gene, -loop_data$loop_width), ]
largest_loops <- largest_loops[!duplicated(largest_loops$gene), ]
largest_loops <- largest_loops %>% dplyr::mutate(loopID = paste(loop_chr, loop_start, loop_end, sep = \_\))
largest_loops <- largest_loops[order(largest_loops$loop_chr, largest_loops$loop_start),]
rownames(largest_loops) <- NULL
# Checking smaller loops
allLoopsGr <- makeGRangesFromDataFrame(allLoops,
seqnames.field = \chrom1\,
start.field = \start1\,
end.field = \end2\)
largestLoopsGr <- makeGRangesFromDataFrame(largest_loops,
seqnames.field = \loop_chr\,
start.field = \loop_start\,
end.field = \loop_end\)
largestLoopsGr <- sort(unique(largestLoopsGr))
complete_overlaps <- subsetByOverlaps(allLoopsGr, largestLoopsGr, type = \within\)
overlaps <- findOverlaps(complete_overlaps, largestLoopsGr)
completeOverlapDf <- as.data.frame(complete_overlaps) %>% dplyr::mutate(loopIDAll = paste(seqnames, start, end, sep = \_\))
largestLoopsDf <- as.data.frame(largestLoopsGr) %>% dplyr::mutate(loopID = paste(seqnames, start, end, sep = \_\))
loopIDpairs <- data.frame(loopID = completeOverlapDf$loopIDAll[queryHits(overlaps)],
loopID2 = largestLoopsDf$loopID[subjectHits(overlaps)])
allLoopsAnnotated <- allLoops %>% dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \_\)) %>% dplyr::left_join(loopIDpairs, by = c(\loopID\))
temp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\P-E\, \P-P\, \E-E\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))
ggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = hasSS), fun = mean,
geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\) +
ggtitle(\PE-PE\) + geom_hline(yintercept = 0)
# Counting loop types
allLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%
dplyr::filter(!(loopID == loopID2))
temp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)
result <- temp %>%
group_by(loopID2, Anno2) %>%
summarise(count = n(), .groups = 'drop') %>%
group_by(loopID2) %>%
mutate(frequency = count / sum(count)) %>%
arrange(loopID2, Anno2)
result <- largest_loops %>% dplyr::left_join(result, by = c(\loopID\ = \loopID2\))
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(result, by = c(\gene\))
data <- data %>% filter(complete.cases(.))
all_anno2 <- unique(data$Anno2)
# Calculate average frequency of Anno2 for each gene in each group
result <- data %>%
# Ensure all possible Anno2 values are present for each gene and group
complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>%
dplyr::mutate(group = case_when(
gene %in% gene.group1 ~ \group1\,
gene %in% gene.group2 ~ \group2\,
TRUE ~ NA
)) %>%
dplyr::select(group, Anno2, frequency)
ggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()
```
```
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###[3.39] Splitting bedpe
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# View results\nggplot(largest_loops, aes(x = loop_width)) + geom_histogram()\n\n\n### Checking how many genes from each group has encompassing S-S\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n)\n\ndata_summary <- data %>%\n  group_by(group) %>%\n  summarize(\n    total = n(),\n    hasSS_yes = sum(hasSS == \\YES\\),\n    percentage_yes = (hasSS_yes / total) * 100\n  )\n\nggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +\n  geom_bar(stat = \\identity\\, color = \\black\\) +\n  labs(\n    title = \\Percentage of Each Group with hasSS = YES\\,\n    x = \\Group\\,\n    y = \\Percentage (%)\\\n  ) + ylim(0, 100) +\n  theme_minimal()\n\n\ntemp <- data %>% dplyr::filter(hasSS == \\YES\\)\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, \\group1\\, \\group2\\))\n\nggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\)\n\n### Comparing RNA perturbation\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(c(1, 4, 5)) %>%\n  dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \\hasSS\\, \\noSS\\))\n\n\nggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +\n  stat_ecdf(size = 0.4) +\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\n\n### Checking ChIP peak density\nlargest_loopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                            seqnames.field = \\loop_chr\\, \n                                            start.field = \\loop_start\\, \n                                            end.field = \\loop_end\\, \n                                            keep.extra.columns = TRUE)\n\n# Find overlaps between ChIP-seq peaks and the largest loops\nchip_overlaps <- findOverlaps(largest_loopsGr, peak.CTCF)\n\n# Count the number of ChIP-seq peaks per loop\nloop_peak_counts <- table(queryHits(chip_overlaps))\n\n# Create a data frame with the counts and loop details\nlargest_loops$density <- 0  # Initialize density column\nlargest_loops$count <- 0    # Initialize peak count column\n\n# Add peak counts to the corresponding loops\nlargest_loops[as.numeric(names(loop_peak_counts)), \\count\\] <- as.integer(loop_peak_counts)\n\n# Calculate the density (peaks per kilobase)\nlargest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)\n\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n) %>% dplyr::filter(hasSS == \\YES\\)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$density\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$density\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\npv <- convPvalue(getPvalWilcox(data, \\group1\\, \\group2\\))\n\nggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = group), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\CTCF\\)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# View results
ggplot(largest_loops, aes(x = loop_width)) + geom_histogram()
### Checking how many genes from each group has encompassing S-S
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(largest_loops, by = c(\gene\))
data <- data %>% dplyr::mutate(
hasSS = ifelse(is.na(loop_width), \NO\, \YES\)
)
data_summary <- data %>%
group_by(group) %>%
summarize(
total = n(),
hasSS_yes = sum(hasSS == \YES\),
percentage_yes = (hasSS_yes / total) * 100
)
ggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +
geom_bar(stat = \identity\, color = \black\) +
labs(
title = \Percentage of Each Group with hasSS = YES\,
x = \Group\,
y = \Percentage (%)\
) + ylim(0, 100) +
theme_minimal()
temp <- data %>% dplyr::filter(hasSS == \YES\)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width
distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(temp, \group1\, \group2\))
ggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\)
### Comparing RNA perturbation
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(c(1, 4, 5)) %>%
dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \hasSS\, \noSS\))
ggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +
stat_ecdf(size = 0.4) +
labs(
x = \Absolute log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
### Checking ChIP peak density
largest_loopsGr <- makeGRangesFromDataFrame(largest_loops,
seqnames.field = \loop_chr\,
start.field = \loop_start\,
end.field = \loop_end\,
keep.extra.columns = TRUE)
# Find overlaps between ChIP-seq peaks and the largest loops
chip_overlaps <- findOverlaps(largest_loopsGr, peak.CTCF)
# Count the number of ChIP-seq peaks per loop
loop_peak_counts <- table(queryHits(chip_overlaps))
# Create a data frame with the counts and loop details
largest_loops$density <- 0 # Initialize density column
largest_loops$count <- 0 # Initialize peak count column
# Add peak counts to the corresponding loops
largest_loops[as.numeric(names(loop_peak_counts)), \count\] <- as.integer(loop_peak_counts)
# Calculate the density (peaks per kilobase)
largest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(largest_loops, by = c(\gene\))
data <- data %>% dplyr::mutate(
hasSS = ifelse(is.na(loop_width), \NO\, \YES\)
) %>% dplyr::filter(hasSS == \YES\)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$density
distance2 <- (data %>% dplyr::filter(group ==group2) )$density
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(data, \group1\, \group2\))
ggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\) +
ggtitle(\CTCF\)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# View results\nggplot(largest_loops, aes(x = loop_width)) + geom_histogram()\n\n\n### Checking how many genes from each group has encompassing S-S\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n)\n\ndata_summary <- data %>%\n  group_by(group) %>%\n  summarize(\n    total = n(),\n    hasSS_yes = sum(hasSS == \\YES\\),\n    percentage_yes = (hasSS_yes / total) * 100\n  )\n\nggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +\n  geom_bar(stat = \\identity\\, color = \\black\\) +\n  labs(\n    title = \\Percentage of Each Group with hasSS = YES\\,\n    x = \\Group\\,\n    y = \\Percentage (%)\\\n  ) + ylim(0, 100) +\n  theme_minimal()\n\n\ntemp <- data %>% dplyr::filter(hasSS == \\YES\\)\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, \\group1\\, \\group2\\))\n\nggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\)\n\n### Comparing RNA perturbation\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(c(1, 4, 5)) %>%\n  dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \\hasSS\\, \\noSS\\))\n\n\nggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +\n  stat_ecdf(size = 0.4) +\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\n\n### Checking ChIP peak density\nlargest_loopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                            seqnames.field = \\loop_chr\\, \n                                            start.field = \\loop_start\\, \n                                            end.field = \\loop_end\\, \n                                            keep.extra.columns = TRUE)\n\n# Find overlaps between ChIP-seq peaks and the largest loops\nchip_overlaps <- findOverlaps(largest_loopsGr, peak.CTCF)\n\n# Count the number of ChIP-seq peaks per loop\nloop_peak_counts <- table(queryHits(chip_overlaps))\n\n# Create a data frame with the counts and loop details\nlargest_loops$density <- 0  # Initialize density column\nlargest_loops$count <- 0    # Initialize peak count column\n\n# Add peak counts to the corresponding loops\nlargest_loops[as.numeric(names(loop_peak_counts)), \\count\\] <- as.integer(loop_peak_counts)\n\n# Calculate the density (peaks per kilobase)\nlargest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)\n\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n) %>% dplyr::filter(hasSS == \\YES\\)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$density\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$density\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\npv <- convPvalue(getPvalWilcox(data, \\group1\\, \\group2\\))\n\nggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = group), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\CTCF\\)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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-->
```r
```r
# View results
ggplot(largest_loops, aes(x = loop_width)) + geom_histogram()
### Checking how many genes from each group has encompassing S-S
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(largest_loops, by = c(\gene\))
data <- data %>% dplyr::mutate(
hasSS = ifelse(is.na(loop_width), \NO\, \YES\)
)
data_summary <- data %>%
group_by(group) %>%
summarize(
total = n(),
hasSS_yes = sum(hasSS == \YES\),
percentage_yes = (hasSS_yes / total) * 100
)
ggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +
geom_bar(stat = \identity\, color = \black\) +
labs(
title = \Percentage of Each Group with hasSS = YES\,
x = \Group\,
y = \Percentage (%)\
) + ylim(0, 100) +
theme_minimal()
temp <- data %>% dplyr::filter(hasSS == \YES\)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width
distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(temp, \group1\, \group2\))
ggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\)
### Comparing RNA perturbation
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(c(1, 4, 5)) %>%
dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \hasSS\, \noSS\))
ggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +
stat_ecdf(size = 0.4) +
labs(
x = \Absolute log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
### Checking ChIP peak density
largest_loopsGr <- makeGRangesFromDataFrame(largest_loops,
seqnames.field = \loop_chr\,
start.field = \loop_start\,
end.field = \loop_end\,
keep.extra.columns = TRUE)
# Find overlaps between ChIP-seq peaks and the largest loops
chip_overlaps <- findOverlaps(largest_loopsGr, peak.CTCF)
# Count the number of ChIP-seq peaks per loop
loop_peak_counts <- table(queryHits(chip_overlaps))
# Create a data frame with the counts and loop details
largest_loops$density <- 0 # Initialize density column
largest_loops$count <- 0 # Initialize peak count column
# Add peak counts to the corresponding loops
largest_loops[as.numeric(names(loop_peak_counts)), \count\] <- as.integer(loop_peak_counts)
# Calculate the density (peaks per kilobase)
largest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(largest_loops, by = c(\gene\))
data <- data %>% dplyr::mutate(
hasSS = ifelse(is.na(loop_width), \NO\, \YES\)
) %>% dplyr::filter(hasSS == \YES\)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$density
distance2 <- (data %>% dplyr::filter(group ==group2) )$density
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(data, \group1\, \group2\))
ggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\) +
ggtitle(\CTCF\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [3.40] Finding largest encompassing S-S
<!-- rnb-text-end -->
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name <- \chromo_cons_annoHierarchy\
# IMPORT S-S LOOPS
allLoops <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
strLoops <- allLoops %>% dplyr::filter(Anno2 %in% c(\S-S\))
temp <- strLoops %>% dplyr::select(c(1, 2, 3, 5, 6)) # Assuming columns include anchor positions
colnames(temp) <- c(\chrom\, \start1\, \end1\, \start2\, \end2\)
strLoopsGr1 <- makeGRangesFromDataFrame(
temp %>% dplyr::select(chrom, start = start1, end = end1)
)
strLoopsGr2 <- makeGRangesFromDataFrame(
temp %>% dplyr::select(chrom, start = start2, end = end2)
)
# IMPORT CTCF MOTIF ANNOTATION
ctcfMotifs <- fread(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\))
colnames(ctcfMotifs) <- c(\chrom\, \start\, \end\, \motif\)
ctcfGr <- makeGRangesFromDataFrame(ctcfMotifs, keep.extra.columns = TRUE)
# Annotate each loop anchor with CTCF motifs
anchor1_with_ctcf <- findOverlaps(strLoopsGr1, ctcfGr)
anchor2_with_ctcf <- findOverlaps(strLoopsGr2, ctcfGr)
# Get motif information for anchor1 overlaps
anchor1_ctcf_motif <- rep(NA, length(strLoopsGr1)) # Initialize motif vector
anchor1_ctcf_motif[queryHits(anchor1_with_ctcf)] <- ctcfGr[subjectHits(anchor1_with_ctcf)]$motif
# Get motif information for anchor2 overlaps
anchor2_ctcf_motif <- rep(NA, length(strLoopsGr2)) # Initialize motif vector
anchor2_ctcf_motif[queryHits(anchor2_with_ctcf)] <- ctcfGr[subjectHits(anchor2_with_ctcf)]$motif
# Add motif information to strLoops
strLoops <- strLoops %>%
mutate(
anchor1_motif = anchor1_ctcf_motif,
anchor2_motif = anchor2_ctcf_motif
)
# Filter for convergent CTCF interactions (forward in anchor1, reverse in anchor2)
convergent_loops <- strLoops %>%
filter(anchor1_motif == \fwd\ & anchor2_motif == \rev\) %>% dplyr::filter(Anno2 %in% c(\S-S\))
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-->
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```r
```r
name <- \chromo_cons_annoHierarchy\
# IMPORT S-S LOOPS
allLoops <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\))
strLoops <- allLoops %>% dplyr::filter(Anno2 %in% c(\S-S\))
temp <- strLoops %>% dplyr::select(c(1, 2, 3, 5, 6)) # Assuming columns include anchor positions
colnames(temp) <- c(\chrom\, \start1\, \end1\, \start2\, \end2\)
strLoopsGr1 <- makeGRangesFromDataFrame(
temp %>% dplyr::select(chrom, start = start1, end = end1)
)
strLoopsGr2 <- makeGRangesFromDataFrame(
temp %>% dplyr::select(chrom, start = start2, end = end2)
)
# IMPORT CTCF MOTIF ANNOTATION
ctcfMotifs <- fread(here(refDir, \33248_CTCF_07-729_Bruce-4_peaks.mergePeak.motifAnnotated.bed\))
colnames(ctcfMotifs) <- c(\chrom\, \start\, \end\, \motif\)
ctcfGr <- makeGRangesFromDataFrame(ctcfMotifs, keep.extra.columns = TRUE)
# Annotate each loop anchor with CTCF motifs
anchor1_with_ctcf <- findOverlaps(strLoopsGr1, ctcfGr)
anchor2_with_ctcf <- findOverlaps(strLoopsGr2, ctcfGr)
# Get motif information for anchor1 overlaps
anchor1_ctcf_motif <- rep(NA, length(strLoopsGr1)) # Initialize motif vector
anchor1_ctcf_motif[queryHits(anchor1_with_ctcf)] <- ctcfGr[subjectHits(anchor1_with_ctcf)]$motif
# Get motif information for anchor2 overlaps
anchor2_ctcf_motif <- rep(NA, length(strLoopsGr2)) # Initialize motif vector
anchor2_ctcf_motif[queryHits(anchor2_with_ctcf)] <- ctcfGr[subjectHits(anchor2_with_ctcf)]$motif
# Add motif information to strLoops
strLoops <- strLoops %>%
mutate(
anchor1_motif = anchor1_ctcf_motif,
anchor2_motif = anchor2_ctcf_motif
)
# Filter for convergent CTCF interactions (forward in anchor1, reverse in anchor2)
convergent_loops <- strLoops %>%
filter(anchor1_motif == \fwd\ & anchor2_motif == \rev\) %>% dplyr::filter(Anno2 %in% c(\S-S\))
```
```
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-->\n\n```r\n# Extract loop boundary from left to right\ntemp <- convergent_loops %>% dplyr::select(c(1, 2, 6))\ncolnames(temp) <- c(\\chrom\\, \\start\\, \\end\\)\nstrLoopsGr <- makeGRangesFromDataFrame(temp)\n\n# IMPORT TSS\nflankSize <- 10\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6, V5)\ncolnames(gene.tb) <- c(\\chrom\\, \\start\\, \\end\\, \\ensembl\\, \\gene\\)\ngeneGr <- makeGRangesFromDataFrame(gene.tb, keep.extra.columns = TRUE)\n\n\n# Find overlaps between loops and TSS\noverlaps <- findOverlaps(geneGr, strLoopsGr)\n\n# Annotate overlaps\ntss_with_loops <- geneGr[queryHits(overlaps)]\nloops_with_tss <- strLoopsGr[subjectHits(overlaps)]\n\n# Combine into a data frame for processing\nloop_data <- data.frame(gene = tss_with_loops$ensembl,\n                        loop_chr = seqnames(loops_with_tss),\n                        loop_start = start(loops_with_tss),\n                        loop_end = end(loops_with_tss),\n                        loop_width = width(loops_with_tss))\n\n# Identify the largest loop for each gene\nlargest_loops <- loop_data[order(loop_data$gene, -loop_data$loop_width), ]\nlargest_loops <- largest_loops[!duplicated(largest_loops$gene), ]\nlargest_loops <- largest_loops %>% dplyr::mutate(loopID = paste(loop_chr, loop_start, loop_end, sep = \\_\\))\nlargest_loops <- largest_loops[order(largest_loops$loop_chr, largest_loops$loop_start),]\nrownames(largest_loops) <- NULL\n\n# Checking smaller loops\nallLoopsGr <- makeGRangesFromDataFrame(allLoops, \n                                       seqnames.field = \\chrom1\\, \n                                       start.field = \\start1\\, \n                                       end.field = \\end2\\)\nlargestLoopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                           seqnames.field = \\loop_chr\\, \n                                           start.field = \\loop_start\\, \n                                           end.field = \\loop_end\\)\nlargestLoopsGr <- sort(unique(largestLoopsGr))\ncomplete_overlaps <- subsetByOverlaps(allLoopsGr, largestLoopsGr, type = \\within\\)\noverlaps <- findOverlaps(complete_overlaps, largestLoopsGr)\n\n\ncompleteOverlapDf <- as.data.frame(complete_overlaps) %>% dplyr::mutate(loopIDAll = paste(seqnames, start, end, sep = \\_\\))\nlargestLoopsDf <- as.data.frame(largestLoopsGr) %>% dplyr::mutate(loopID = paste(seqnames, start, end, sep = \\_\\))\n\nloopIDpairs <- data.frame(loopID = completeOverlapDf$loopIDAll[queryHits(overlaps)],\n                          loopID2 = largestLoopsDf$loopID[subjectHits(overlaps)])\n\n# allLoopsAnnotated <- allLoops %>% dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\)) %>% dplyr::left_join(loopIDpairs, by = c(\\loopID\\))\n\n\n##################################\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% unnest(gene) %>%\n  dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\))\ntemp <- geneAnnoData %>% dplyr::left_join(loopIDpairs, by = c(\\loopID\\))\ntemp2 <- temp %>% dplyr::select(gene, loopID2) %>%\n  group_by(gene) %>%              # Group data by the 'gene' column\n  summarize(geneHasSS = !all(is.na(loopID2)))  # Check if all loopID2 values for each gene are NA\ntemp <- temp %>% left_join(temp2, by = c(\\gene\\))\ndata.all <- temp %>% dplyr::mutate(group = ifelse(!geneHasSS, \\No\\,\n                                              ifelse(is.na(loopID2), \\Outside\\, \\Within\\)),\n                               size = start2 - start1)\n\n##################################\noutfile <- data.all %>% dplyr::select(loopID2, gene) %>% dplyr::filter(!is.na(loopID2)) %>%\n  separate(loopID2, into = c(\\chrom\\, \\start\\, \\end\\), sep = \\_\\, convert = TRUE)\n# fwrite(outfile, here(consensusDir, \\convergent_ss_domain.bed\\), sep = \\\\t\\, col.names = FALSE)\n```\n\n<!-- rnb-source-end -->\n"} -->
# Extract loop boundary from left to right
temp <- convergent_loops %>% dplyr::select(c(1, 2, 6))
colnames(temp) <- c(\chrom\, \start\, \end\)
strLoopsGr <- makeGRangesFromDataFrame(temp)
# IMPORT TSS
flankSize <- 10
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chrom\, \start\, \end\, \ensembl\, \gene\)
geneGr <- makeGRangesFromDataFrame(gene.tb, keep.extra.columns = TRUE)
# Find overlaps between loops and TSS
overlaps <- findOverlaps(geneGr, strLoopsGr)
# Annotate overlaps
tss_with_loops <- geneGr[queryHits(overlaps)]
loops_with_tss <- strLoopsGr[subjectHits(overlaps)]
# Combine into a data frame for processing
loop_data <- data.frame(gene = tss_with_loops$ensembl,
loop_chr = seqnames(loops_with_tss),
loop_start = start(loops_with_tss),
loop_end = end(loops_with_tss),
loop_width = width(loops_with_tss))
# Identify the largest loop for each gene
largest_loops <- loop_data[order(loop_data$gene, -loop_data$loop_width), ]
largest_loops <- largest_loops[!duplicated(largest_loops$gene), ]
largest_loops <- largest_loops %>% dplyr::mutate(loopID = paste(loop_chr, loop_start, loop_end, sep = \_\))
largest_loops <- largest_loops[order(largest_loops$loop_chr, largest_loops$loop_start),]
rownames(largest_loops) <- NULL
# Checking smaller loops
allLoopsGr <- makeGRangesFromDataFrame(allLoops,
seqnames.field = \chrom1\,
start.field = \start1\,
end.field = \end2\)
largestLoopsGr <- makeGRangesFromDataFrame(largest_loops,
seqnames.field = \loop_chr\,
start.field = \loop_start\,
end.field = \loop_end\)
largestLoopsGr <- sort(unique(largestLoopsGr))
complete_overlaps <- subsetByOverlaps(allLoopsGr, largestLoopsGr, type = \within\)
overlaps <- findOverlaps(complete_overlaps, largestLoopsGr)
completeOverlapDf <- as.data.frame(complete_overlaps) %>% dplyr::mutate(loopIDAll = paste(seqnames, start, end, sep = \_\))
largestLoopsDf <- as.data.frame(largestLoopsGr) %>% dplyr::mutate(loopID = paste(seqnames, start, end, sep = \_\))
loopIDpairs <- data.frame(loopID = completeOverlapDf$loopIDAll[queryHits(overlaps)],
loopID2 = largestLoopsDf$loopID[subjectHits(overlaps)])
# allLoopsAnnotated <- allLoops %>% dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \_\)) %>% dplyr::left_join(loopIDpairs, by = c(\loopID\))
##################################
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\)) %>% unnest(gene) %>%
dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \_\))
temp <- geneAnnoData %>% dplyr::left_join(loopIDpairs, by = c(\loopID\))
temp2 <- temp %>% dplyr::select(gene, loopID2) %>%
group_by(gene) %>% # Group data by the 'gene' column
summarize(geneHasSS = !all(is.na(loopID2))) # Check if all loopID2 values for each gene are NA
temp <- temp %>% left_join(temp2, by = c(\gene\))
data.all <- temp %>% dplyr::mutate(group = ifelse(!geneHasSS, \No\,
ifelse(is.na(loopID2), \Outside\, \Within\)),
size = start2 - start1)
##################################
outfile <- data.all %>% dplyr::select(loopID2, gene) %>% dplyr::filter(!is.na(loopID2)) %>%
separate(loopID2, into = c(\chrom\, \start\, \end\), sep = \_\, convert = TRUE)
# fwrite(outfile, here(consensusDir, \convergent_ss_domain.bed\), sep = \\t\, col.names = FALSE)
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-->\n\n```r\n```r\n# Extract loop boundary from left to right\ntemp <- convergent_loops %>% dplyr::select(c(1, 2, 6))\ncolnames(temp) <- c(\\chrom\\, \\start\\, \\end\\)\nstrLoopsGr <- makeGRangesFromDataFrame(temp)\n\n# IMPORT TSS\nflankSize <- 10\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V1, TSSstart, TSSend, V6, V5)\ncolnames(gene.tb) <- c(\\chrom\\, \\start\\, \\end\\, \\ensembl\\, \\gene\\)\ngeneGr <- makeGRangesFromDataFrame(gene.tb, keep.extra.columns = TRUE)\n\n\n# Find overlaps between loops and TSS\noverlaps <- findOverlaps(geneGr, strLoopsGr)\n\n# Annotate overlaps\ntss_with_loops <- geneGr[queryHits(overlaps)]\nloops_with_tss <- strLoopsGr[subjectHits(overlaps)]\n\n# Combine into a data frame for processing\nloop_data <- data.frame(gene = tss_with_loops$ensembl,\n                        loop_chr = seqnames(loops_with_tss),\n                        loop_start = start(loops_with_tss),\n                        loop_end = end(loops_with_tss),\n                        loop_width = width(loops_with_tss))\n\n# Identify the largest loop for each gene\nlargest_loops <- loop_data[order(loop_data$gene, -loop_data$loop_width), ]\nlargest_loops <- largest_loops[!duplicated(largest_loops$gene), ]\nlargest_loops <- largest_loops %>% dplyr::mutate(loopID = paste(loop_chr, loop_start, loop_end, sep = \\_\\))\nlargest_loops <- largest_loops[order(largest_loops$loop_chr, largest_loops$loop_start),]\nrownames(largest_loops) <- NULL\n\n# Checking smaller loops\nallLoopsGr <- makeGRangesFromDataFrame(allLoops, \n                                       seqnames.field = \\chrom1\\, \n                                       start.field = \\start1\\, \n                                       end.field = \\end2\\)\nlargestLoopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                           seqnames.field = \\loop_chr\\, \n                                           start.field = \\loop_start\\, \n                                           end.field = \\loop_end\\)\nlargestLoopsGr <- sort(unique(largestLoopsGr))\ncomplete_overlaps <- subsetByOverlaps(allLoopsGr, largestLoopsGr, type = \\within\\)\noverlaps <- findOverlaps(complete_overlaps, largestLoopsGr)\n\n\ncompleteOverlapDf <- as.data.frame(complete_overlaps) %>% dplyr::mutate(loopIDAll = paste(seqnames, start, end, sep = \\_\\))\nlargestLoopsDf <- as.data.frame(largestLoopsGr) %>% dplyr::mutate(loopID = paste(seqnames, start, end, sep = \\_\\))\n\nloopIDpairs <- data.frame(loopID = completeOverlapDf$loopIDAll[queryHits(overlaps)],\n                          loopID2 = largestLoopsDf$loopID[subjectHits(overlaps)])\n\n# allLoopsAnnotated <- allLoops %>% dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\)) %>% dplyr::left_join(loopIDpairs, by = c(\\loopID\\))\n\n\n##################################\ndiffCutoff <- 0.2\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = diffCutoff,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\)) %>% unnest(gene) %>%\n  dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\))\ntemp <- geneAnnoData %>% dplyr::left_join(loopIDpairs, by = c(\\loopID\\))\ntemp2 <- temp %>% dplyr::select(gene, loopID2) %>%\n  group_by(gene) %>%              # Group data by the 'gene' column\n  summarize(geneHasSS = !all(is.na(loopID2)))  # Check if all loopID2 values for each gene are NA\ntemp <- temp %>% left_join(temp2, by = c(\\gene\\))\ndata.all <- temp %>% dplyr::mutate(group = ifelse(!geneHasSS, \\No\\,\n                                              ifelse(is.na(loopID2), \\Outside\\, \\Within\\)),\n                               size = start2 - start1)\n\n##################################\noutfile <- data.all %>% dplyr::select(loopID2, gene) %>% dplyr::filter(!is.na(loopID2)) %>%\n  separate(loopID2, into = c(\\chrom\\, \\start\\, \\end\\), sep = \\_\\, convert = TRUE)\n# fwrite(outfile, here(consensusDir, \\convergent_ss_domain.bed\\), sep = \\\\t\\, col.names = FALSE)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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-->
```r
```r
# Extract loop boundary from left to right
temp <- convergent_loops %>% dplyr::select(c(1, 2, 6))
colnames(temp) <- c(\chrom\, \start\, \end\)
strLoopsGr <- makeGRangesFromDataFrame(temp)
# IMPORT TSS
flankSize <- 10
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V1, TSSstart, TSSend, V6, V5)
colnames(gene.tb) <- c(\chrom\, \start\, \end\, \ensembl\, \gene\)
geneGr <- makeGRangesFromDataFrame(gene.tb, keep.extra.columns = TRUE)
# Find overlaps between loops and TSS
overlaps <- findOverlaps(geneGr, strLoopsGr)
# Annotate overlaps
tss_with_loops <- geneGr[queryHits(overlaps)]
loops_with_tss <- strLoopsGr[subjectHits(overlaps)]
# Combine into a data frame for processing
loop_data <- data.frame(gene = tss_with_loops$ensembl,
loop_chr = seqnames(loops_with_tss),
loop_start = start(loops_with_tss),
loop_end = end(loops_with_tss),
loop_width = width(loops_with_tss))
# Identify the largest loop for each gene
largest_loops <- loop_data[order(loop_data$gene, -loop_data$loop_width), ]
largest_loops <- largest_loops[!duplicated(largest_loops$gene), ]
largest_loops <- largest_loops %>% dplyr::mutate(loopID = paste(loop_chr, loop_start, loop_end, sep = \_\))
largest_loops <- largest_loops[order(largest_loops$loop_chr, largest_loops$loop_start),]
rownames(largest_loops) <- NULL
# Checking smaller loops
allLoopsGr <- makeGRangesFromDataFrame(allLoops,
seqnames.field = \chrom1\,
start.field = \start1\,
end.field = \end2\)
largestLoopsGr <- makeGRangesFromDataFrame(largest_loops,
seqnames.field = \loop_chr\,
start.field = \loop_start\,
end.field = \loop_end\)
largestLoopsGr <- sort(unique(largestLoopsGr))
complete_overlaps <- subsetByOverlaps(allLoopsGr, largestLoopsGr, type = \within\)
overlaps <- findOverlaps(complete_overlaps, largestLoopsGr)
completeOverlapDf <- as.data.frame(complete_overlaps) %>% dplyr::mutate(loopIDAll = paste(seqnames, start, end, sep = \_\))
largestLoopsDf <- as.data.frame(largestLoopsGr) %>% dplyr::mutate(loopID = paste(seqnames, start, end, sep = \_\))
loopIDpairs <- data.frame(loopID = completeOverlapDf$loopIDAll[queryHits(overlaps)],
loopID2 = largestLoopsDf$loopID[subjectHits(overlaps)])
# allLoopsAnnotated <- allLoops %>% dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \_\)) %>% dplyr::left_join(loopIDpairs, by = c(\loopID\))
##################################
diffCutoff <- 0.2
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = diffCutoff,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\)) %>% unnest(gene) %>%
dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \_\))
temp <- geneAnnoData %>% dplyr::left_join(loopIDpairs, by = c(\loopID\))
temp2 <- temp %>% dplyr::select(gene, loopID2) %>%
group_by(gene) %>% # Group data by the 'gene' column
summarize(geneHasSS = !all(is.na(loopID2))) # Check if all loopID2 values for each gene are NA
temp <- temp %>% left_join(temp2, by = c(\gene\))
data.all <- temp %>% dplyr::mutate(group = ifelse(!geneHasSS, \No\,
ifelse(is.na(loopID2), \Outside\, \Within\)),
size = start2 - start1)
##################################
outfile <- data.all %>% dplyr::select(loopID2, gene) %>% dplyr::filter(!is.na(loopID2)) %>%
separate(loopID2, into = c(\chrom\, \start\, \end\), sep = \_\, convert = TRUE)
# fwrite(outfile, here(consensusDir, \convergent_ss_domain.bed\), sep = \\t\, col.names = FALSE)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [3.41] Finding largest convergent encompassing S-S
#### Filtering convergent S-S loops
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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alWilcox(temp, \"Outside\", \"Within\"))\n\np <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium * mmToLineUnit, lineend = \"square\", alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \"black\",\n               linewidth = lineMedium * mmToLineUnit, lineend = \"square\",\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() +   \n    scale_fill_manual(values = c(\"#777777\", \"#F28E2C\", \"#F28E2C\")) +\n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \"point\", shape = 21, size = 0.5,\n    fill = \"black\", color = \"black\", position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \"Δ loop score\") +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\") + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\", linetype = \"dashed\") +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\"insulationBoundary_delta_convSS_pe\")\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n###################### \n\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\"loopScore_cons_obsexp.tsv\"))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ndata2 <- data %>% left_join(obsexp, by = c(\"id\"))\n\ntemp <- data2 %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nconvPvalue(getPvalWilcox(temp, \"No\", \"Within\"))\nconvPvalue(getPvalWilcox(temp, \"No\", \"Outside\"))\nconvPvalue(getPvalWilcox(temp, \"Outside\", \"Within\"))\n\n\np <- ggplot(temp, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \"square\", alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \"black\",\n               linewidth = lineMedium * mmToLineUnit, lineend = \"square\",\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  scale_fill_manual(values = c(\"#777777\", \"#F28E2C\", \"#F28E2C\")) +\n  \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \"point\", shape = 21, size = 0.5,\n    fill = \"black\", color = \"black\", position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \"log2(fc of obs/exp)\") +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \"black\",\n             size = lineThick*mmToLineUnit,\n             lineend = \"square\") + \n  coord_cartesian(ylim = c(-2, 2))\n    width <-30*mmToInch\nheight <- 35*mmToInch\nfileName <- paste0(\"insulationBoundary_obsexp_convSS_pe\")\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\ndata <- data.all\ngene.withinBoundary <- unique((data %>% dplyr::filter(group == \"Within\"))$gene)\ngene.outsideBoundary <- unique((data %>% dplyr::filter(group == \"Outside\"))$gene)\ngene.noBoundary <- unique((data %>% dplyr::filter(group == \"No\"))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \"geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\"))$gene\ngene.group2 <- fread(here(refDir, \"geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\"))$gene\n\nprint(\"perc of gene with loop within Boundary\")\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\"perc of gene with loop outside Boundary\")\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\"perc of gene with loop with no Boundary\")\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \"diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\")) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \"withinBoundary\",\n    ensembl_gene_id %in% gene.noBoundary ~ \"noBoundary\",\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\nks_result <- ks.test(\n  diff.RNA %>% dplyr::filter(group == \"withinBoundary\") %>% pull(absLog2FC),\n  diff.RNA %>% dplyr::filter(group == \"noBoundary\") %>% pull(absLog2FC)\n)\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n#   distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# convPvalue(getPvalWilcox(diff.RNA, \"withBoundary\", \"noBoundary\"))\n# \n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\n  scale_color_manual(values = (c(\"#777777\", \"#F28E2C\"))) +\n\nstat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \"square\"  ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \"Abs. log2(fold change)\",\n    y = \"Cumulative Probability\"\n  ) + coord_cartesian(xlim = c(0, 1)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \"#000000\"\n    ),\n    axis.line = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\",\n      size = lineThick*mmToLineUnit,\n      lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.position = \"none\",\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\"log2FC_cdf_insulationBoundary_convSS_pe\")\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \".png\")), res = 600, unit = \"in\", height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()\n```"} -->\n\n```r\ndata <- data.all %>% dplyr::filter(Anno2 %in% c(\\P-E\\))\n\ntemp <- data %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()\n\ntemp1 <- temp %>% dplyr::filter(group == \\No\\) %>% dplyr::select(-group)\nfwrite(temp1, here(consensusDir, \\insulated_domain_ss_no.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp2 <- temp %>% dplyr::filter(group == \\Within\\) %>% dplyr::select(-group)\nfwrite(temp2, here(consensusDir, \\insulated_domain_ss_within.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp3 <- temp %>% dplyr::filter(group == \\Outside\\) %>% dplyr::select(-group)\nfwrite(temp3, here(consensusDir, \\insulated_domain_ss_outside.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- data %>% dplyr::select(id, group, size) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$size\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$size\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\n\np <- ggplot(temp, aes(x = group, y = size, fill = group)) + \n  geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n  scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Loop size\\)\n\nfileName <- paste0(\\insulationBoundary_size_convSS_PE\\)\nwidth <-33*mmToInch\nheight <- 35*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntemp <- data %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\np <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() +   \n    scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\) +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\\insulationBoundary_delta_convSS_pe\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n###################### \n\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ndata2 <- data %>% left_join(obsexp, by = c(\\id\\))\n\ntemp <- data2 %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\n\np <- ggplot(temp, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\) +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n    width <-30*mmToInch\nheight <- 35*mmToInch\nfileName <- paste0(\\insulationBoundary_obsexp_convSS_pe\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\ndata <- data.all\ngene.withinBoundary <- unique((data %>% dplyr::filter(group == \\Within\\))$gene)\ngene.outsideBoundary <- unique((data %>% dplyr::filter(group == \\Outside\\))$gene)\ngene.noBoundary <- unique((data %>% dplyr::filter(group == \\No\\))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nprint(\\perc of gene with loop within Boundary\\)\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop outside Boundary\\)\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop with no Boundary\\)\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \\withinBoundary\\,\n    ensembl_gene_id %in% gene.noBoundary ~ \\noBoundary\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\nks_result <- ks.test(\n  diff.RNA %>% dplyr::filter(group == \\withinBoundary\\) %>% pull(absLog2FC),\n  diff.RNA %>% dplyr::filter(group == \\noBoundary\\) %>% pull(absLog2FC)\n)\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n#   distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# convPvalue(getPvalWilcox(diff.RNA, \\withBoundary\\, \\noBoundary\\))\n# \n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\n  scale_color_manual(values = (c(\\#777777\\, \\#F28E2C\\))) +\n\nstat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\  ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\\log2FC_cdf_insulationBoundary_convSS_pe\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
data <- data.all %>% dplyr::filter(Anno2 %in% c(\P-E\))
temp <- data %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()
temp1 <- temp %>% dplyr::filter(group == \No\) %>% dplyr::select(-group)
fwrite(temp1, here(consensusDir, \insulated_domain_ss_no.bedpe\), sep = \\t\, col.names = FALSE)
temp2 <- temp %>% dplyr::filter(group == \Within\) %>% dplyr::select(-group)
fwrite(temp2, here(consensusDir, \insulated_domain_ss_within.bedpe\), sep = \\t\, col.names = FALSE)
temp3 <- temp %>% dplyr::filter(group == \Outside\) %>% dplyr::select(-group)
fwrite(temp3, here(consensusDir, \insulated_domain_ss_outside.bedpe\), sep = \\t\, col.names = FALSE)
temp <- data %>% dplyr::select(id, group, size) %>% distinct()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$size
distance2 <- (data %>% dplyr::filter(group ==group2) )$size
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
convPvalue(getPvalWilcox(temp, \No\, \Within\))
convPvalue(getPvalWilcox(temp, \No\, \Outside\))
convPvalue(getPvalWilcox(temp, \Outside\, \Within\))
p <- ggplot(temp, aes(x = group, y = size, fill = group)) +
geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() + scale_y_continuous(labels = label_kb_mb) +
scale_fill_manual(values = c(\#777777\, \#F28E2C\, \#F28E2C\)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Loop size\)
fileName <- paste0(\insulationBoundary_size_convSS_PE\)
width <-33*mmToInch
height <- 35*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
temp <- data %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()
convPvalue(getPvalWilcox(temp, \No\, \Within\))
convPvalue(getPvalWilcox(temp, \No\, \Outside\))
convPvalue(getPvalWilcox(temp, \Outside\, \Within\))
p <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineMedium * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
scale_fill_manual(values = c(\#777777\, \#F28E2C\, \#F28E2C\)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Δ loop score\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = -0.2,
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\) +
coord_cartesian(ylim = c(-0.8, 0.5))
fileName <- paste0(\insulationBoundary_delta_convSS_pe\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
######################
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
data2 <- data %>% left_join(obsexp, by = c(\id\))
temp <- data2 %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
convPvalue(getPvalWilcox(temp, \No\, \Within\))
convPvalue(getPvalWilcox(temp, \No\, \Outside\))
convPvalue(getPvalWilcox(temp, \Outside\, \Within\))
p <- ggplot(temp, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
scale_fill_manual(values = c(\#777777\, \#F28E2C\, \#F28E2C\)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2(fc of obs/exp)\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
coord_cartesian(ylim = c(-2, 2))
width <-30*mmToInch
height <- 35*mmToInch
fileName <- paste0(\insulationBoundary_obsexp_convSS_pe\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#####################
data <- data.all
gene.withinBoundary <- unique((data %>% dplyr::filter(group == \Within\))$gene)
gene.outsideBoundary <- unique((data %>% dplyr::filter(group == \Outside\))$gene)
gene.noBoundary <- unique((data %>% dplyr::filter(group == \No\))$gene)
gene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
print(\perc of gene with loop within Boundary\)
sum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100
print(\perc of gene with loop outside Boundary\)
sum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100
print(\perc of gene with loop with no Boundary\)
sum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%
dplyr::mutate(group = case_when(
ensembl_gene_id %in% gene.wBoundary ~ \withinBoundary\,
ensembl_gene_id %in% gene.noBoundary ~ \noBoundary\,
TRUE ~ NA
)) %>%
dplyr::filter(!is.na(group)) %>%
dplyr::mutate(absLog2FC = abs(log2FoldChange))
ks_result <- ks.test(
diff.RNA %>% dplyr::filter(group == \withinBoundary\) %>% pull(absLog2FC),
diff.RNA %>% dplyr::filter(group == \noBoundary\) %>% pull(absLog2FC)
)
ggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC
# distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
# convPvalue(getPvalWilcox(diff.RNA, \withBoundary\, \noBoundary\))
#
# Create the CDF plot
p <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +
scale_color_manual(values = (c(\#777777\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\log2FC_cdf_insulationBoundary_convSS_pe\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\np <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() +   \n    scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\) +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\\insulationBoundary_delta_convSS_pe\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n###################### \n\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ndata2 <- data %>% left_join(obsexp, by = c(\\id\\))\n\ntemp <- data2 %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\n\np <- ggplot(temp, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\) +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n    width <-30*mmToInch\nheight <- 35*mmToInch\nfileName <- paste0(\\insulationBoundary_obsexp_convSS_pe\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\ndata <- data.all\ngene.withinBoundary <- unique((data %>% dplyr::filter(group == \\Within\\))$gene)\ngene.outsideBoundary <- unique((data %>% dplyr::filter(group == \\Outside\\))$gene)\ngene.noBoundary <- unique((data %>% dplyr::filter(group == \\No\\))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nprint(\\perc of gene with loop within Boundary\\)\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop outside Boundary\\)\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop with no Boundary\\)\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \\withinBoundary\\,\n    ensembl_gene_id %in% gene.noBoundary ~ \\noBoundary\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\nks_result <- ks.test(\n  diff.RNA %>% dplyr::filter(group == \\withinBoundary\\) %>% pull(absLog2FC),\n  diff.RNA %>% dplyr::filter(group == \\noBoundary\\) %>% pull(absLog2FC)\n)\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n#   distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# convPvalue(getPvalWilcox(diff.RNA, \\withBoundary\\, \\noBoundary\\))\n# \n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\n  scale_color_manual(values = (c(\\#777777\\, \\#F28E2C\\))) +\n\nstat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\  ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\\log2FC_cdf_insulationBoundary_convSS_pe\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->\n\n```r\n```r\ndata <- data.all %>% dplyr::filter(Anno2 %in% c(\\P-E\\))\n\ntemp <- data %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()\n\ntemp1 <- temp %>% dplyr::filter(group == \\No\\) %>% dplyr::select(-group)\nfwrite(temp1, here(consensusDir, \\insulated_domain_ss_no.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp2 <- temp %>% dplyr::filter(group == \\Within\\) %>% dplyr::select(-group)\nfwrite(temp2, here(consensusDir, \\insulated_domain_ss_within.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp3 <- temp %>% dplyr::filter(group == \\Outside\\) %>% dplyr::select(-group)\nfwrite(temp3, here(consensusDir, \\insulated_domain_ss_outside.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- data %>% dplyr::select(id, group, size) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$size\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$size\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\n\np <- ggplot(temp, aes(x = group, y = size, fill = group)) + \n  geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n  scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Loop size\\)\n\nfileName <- paste0(\\insulationBoundary_size_convSS_PE\\)\nwidth <-33*mmToInch\nheight <- 35*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntemp <- data %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\np <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() +   \n    scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\) +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\\insulationBoundary_delta_convSS_pe\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n###################### \n\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ndata2 <- data %>% left_join(obsexp, by = c(\\id\\))\n\ntemp <- data2 %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\n\np <- ggplot(temp, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\) +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n    width <-30*mmToInch\nheight <- 35*mmToInch\nfileName <- paste0(\\insulationBoundary_obsexp_convSS_pe\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\ndata <- data.all\ngene.withinBoundary <- unique((data %>% dplyr::filter(group == \\Within\\))$gene)\ngene.outsideBoundary <- unique((data %>% dplyr::filter(group == \\Outside\\))$gene)\ngene.noBoundary <- unique((data %>% dplyr::filter(group == \\No\\))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nprint(\\perc of gene with loop within Boundary\\)\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop outside Boundary\\)\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop with no Boundary\\)\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \\withinBoundary\\,\n    ensembl_gene_id %in% gene.noBoundary ~ \\noBoundary\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\nks_result <- ks.test(\n  diff.RNA %>% dplyr::filter(group == \\withinBoundary\\) %>% pull(absLog2FC),\n  diff.RNA %>% dplyr::filter(group == \\noBoundary\\) %>% pull(absLog2FC)\n)\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n#   distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# convPvalue(getPvalWilcox(diff.RNA, \\withBoundary\\, \\noBoundary\\))\n# \n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\n  scale_color_manual(values = (c(\\#777777\\, \\#F28E2C\\))) +\n\nstat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\  ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\\log2FC_cdf_insulationBoundary_convSS_pe\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ndata <- data.all %>% dplyr::filter(Anno2 %in% c(\\P-E\\))\n\ntemp <- data %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()\n\ntemp1 <- temp %>% dplyr::filter(group == \\No\\) %>% dplyr::select(-group)\nfwrite(temp1, here(consensusDir, \\insulated_domain_ss_no.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp2 <- temp %>% dplyr::filter(group == \\Within\\) %>% dplyr::select(-group)\nfwrite(temp2, here(consensusDir, \\insulated_domain_ss_within.bedpe\\), sep = \\\\t\\, col.names = FALSE)\ntemp3 <- temp %>% dplyr::filter(group == \\Outside\\) %>% dplyr::select(-group)\nfwrite(temp3, here(consensusDir, \\insulated_domain_ss_outside.bedpe\\), sep = \\\\t\\, col.names = FALSE)\n\ntemp <- data %>% dplyr::select(id, group, size) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$size\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$size\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\n\np <- ggplot(temp, aes(x = group, y = size, fill = group)) + \n  geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n                 linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n                 outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + scale_y_continuous(labels = label_kb_mb) +\n  scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  stat_summary(\n      aes(group = group), fun = mean,\n      geom = \\point\\, shape = 21, size = 0.5,\n      fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Loop size\\)\n\nfileName <- paste0(\\insulationBoundary_size_convSS_PE\\)\nwidth <-33*mmToInch\nheight <- 35*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\ntemp <- data %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\np <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() +   \n    scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\) +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.8, 0.5))\n    \nfileName <- paste0(\\insulationBoundary_delta_convSS_pe\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n###################### \n\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\ndata2 <- data %>% left_join(obsexp, by = c(\\id\\))\n\ntemp <- data2 %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()\n\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Within\\))\nconvPvalue(getPvalWilcox(temp, \\No\\, \\Outside\\))\nconvPvalue(getPvalWilcox(temp, \\Outside\\, \\Within\\))\n\n\np <- ggplot(temp, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  scale_fill_manual(values = c(\\#777777\\, \\#F28E2C\\, \\#F28E2C\\)) +\n  \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\) +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n    width <-30*mmToInch\nheight <- 35*mmToInch\nfileName <- paste0(\\insulationBoundary_obsexp_convSS_pe\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n\n\n\n\n#####################\n\ndata <- data.all\ngene.withinBoundary <- unique((data %>% dplyr::filter(group == \\Within\\))$gene)\ngene.outsideBoundary <- unique((data %>% dplyr::filter(group == \\Outside\\))$gene)\ngene.noBoundary <- unique((data %>% dplyr::filter(group == \\No\\))$gene)\n\ngene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\nprint(\\perc of gene with loop within Boundary\\)\nsum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop outside Boundary\\)\nsum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100\n\nprint(\\perc of gene with loop with no Boundary\\)\nsum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100\nsum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100\n\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%\n  dplyr::mutate(group = case_when(\n    ensembl_gene_id %in% gene.wBoundary ~ \\withinBoundary\\,\n    ensembl_gene_id %in% gene.noBoundary ~ \\noBoundary\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group)) %>%\n  dplyr::mutate(absLog2FC = abs(log2FoldChange))\nks_result <- ks.test(\n  diff.RNA %>% dplyr::filter(group == \\withinBoundary\\) %>% pull(absLog2FC),\n  diff.RNA %>% dplyr::filter(group == \\noBoundary\\) %>% pull(absLog2FC)\n)\n\nggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)\n\n# getPvalWilcox <- function(data, group1, group2){\n#   distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC\n#   distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC\n#   wil <- wilcox.test(distance1, distance2)\n#   return(wil$p.value)\n# }\n# convPvalue(getPvalWilcox(diff.RNA, \\withBoundary\\, \\noBoundary\\))\n# \n\n# Create the CDF plot\np <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +\n  scale_color_manual(values = (c(\\#777777\\, \\#F28E2C\\))) +\n\nstat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \\square\\  ) + # Use stat_ecdf to plot the empirical CDF\n  labs(\n    x = \\Abs. log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.position = \\none\\,\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\nfileName <- paste0(\\log2FC_cdf_insulationBoundary_convSS_pe\\)\nwidth <- 33*mmToInch\nheight <-33*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```"} -->
```r
```r
data <- data.all %>% dplyr::filter(Anno2 %in% c(\P-E\))
temp <- data %>% dplyr::select(chrom1, start1, end1, chrom2, start2, end2, group) %>% distinct()
temp1 <- temp %>% dplyr::filter(group == \No\) %>% dplyr::select(-group)
fwrite(temp1, here(consensusDir, \insulated_domain_ss_no.bedpe\), sep = \\t\, col.names = FALSE)
temp2 <- temp %>% dplyr::filter(group == \Within\) %>% dplyr::select(-group)
fwrite(temp2, here(consensusDir, \insulated_domain_ss_within.bedpe\), sep = \\t\, col.names = FALSE)
temp3 <- temp %>% dplyr::filter(group == \Outside\) %>% dplyr::select(-group)
fwrite(temp3, here(consensusDir, \insulated_domain_ss_outside.bedpe\), sep = \\t\, col.names = FALSE)
temp <- data %>% dplyr::select(id, group, size) %>% distinct()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$size
distance2 <- (data %>% dplyr::filter(group ==group2) )$size
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
convPvalue(getPvalWilcox(temp, \No\, \Within\))
convPvalue(getPvalWilcox(temp, \No\, \Outside\))
convPvalue(getPvalWilcox(temp, \Outside\, \Within\))
p <- ggplot(temp, aes(x = group, y = size, fill = group)) +
geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() + scale_y_continuous(labels = label_kb_mb) +
scale_fill_manual(values = c(\#777777\, \#F28E2C\, \#F28E2C\)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Loop size\)
fileName <- paste0(\insulationBoundary_size_convSS_PE\)
width <-33*mmToInch
height <- 35*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(group ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
temp <- data %>% dplyr::select(group, id, diff_dTAG_DMSO) %>% distinct()
convPvalue(getPvalWilcox(temp, \No\, \Within\))
convPvalue(getPvalWilcox(temp, \No\, \Outside\))
convPvalue(getPvalWilcox(temp, \Outside\, \Within\))
p <- ggplot(data, aes(x = group, y = diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineMedium * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
scale_fill_manual(values = c(\#777777\, \#F28E2C\, \#F28E2C\)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Δ loop score\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = -0.2,
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\) +
coord_cartesian(ylim = c(-0.8, 0.5))
fileName <- paste0(\insulationBoundary_delta_convSS_pe\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
######################
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
data2 <- data %>% left_join(obsexp, by = c(\id\))
temp <- data2 %>% dplyr::select(group, id, log_obsexp_diff_dTAG_DMSO) %>% distinct()
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$log_obsexp_diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(group ==group2) )$log_obsexp_diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
convPvalue(getPvalWilcox(temp, \No\, \Within\))
convPvalue(getPvalWilcox(temp, \No\, \Outside\))
convPvalue(getPvalWilcox(temp, \Outside\, \Within\))
p <- ggplot(temp, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineMedium* mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
scale_fill_manual(values = c(\#777777\, \#F28E2C\, \#F28E2C\)) +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2(fc of obs/exp)\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
coord_cartesian(ylim = c(-2, 2))
width <-30*mmToInch
height <- 35*mmToInch
fileName <- paste0(\insulationBoundary_obsexp_convSS_pe\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
#####################
data <- data.all
gene.withinBoundary <- unique((data %>% dplyr::filter(group == \Within\))$gene)
gene.outsideBoundary <- unique((data %>% dplyr::filter(group == \Outside\))$gene)
gene.noBoundary <- unique((data %>% dplyr::filter(group == \No\))$gene)
gene.wBoundary <- unique(c(gene.withinBoundary, gene.outsideBoundary))
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
print(\perc of gene with loop within Boundary\)
sum(gene.group1 %in% gene.withinBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.withinBoundary)/length(gene.group2)*100
print(\perc of gene with loop outside Boundary\)
sum(gene.group1 %in% gene.outsideBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.outsideBoundary)/length(gene.group2)*100
print(\perc of gene with loop with no Boundary\)
sum(gene.group1 %in% gene.noBoundary)/length(gene.group1)*100
sum(gene.group2 %in% gene.noBoundary)/length(gene.group2)*100
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name) %>%
dplyr::mutate(group = case_when(
ensembl_gene_id %in% gene.wBoundary ~ \withinBoundary\,
ensembl_gene_id %in% gene.noBoundary ~ \noBoundary\,
TRUE ~ NA
)) %>%
dplyr::filter(!is.na(group)) %>%
dplyr::mutate(absLog2FC = abs(log2FoldChange))
ks_result <- ks.test(
diff.RNA %>% dplyr::filter(group == \withinBoundary\) %>% pull(absLog2FC),
diff.RNA %>% dplyr::filter(group == \noBoundary\) %>% pull(absLog2FC)
)
ggplot(diff.RNA, aes(x = group, y = log2FoldChange)) + geom_violin() + geom_boxplot(width = 0.1)
# getPvalWilcox <- function(data, group1, group2){
# distance1 <- (data %>% dplyr::filter(group ==group1) )$absLog2FC
# distance2 <- (data %>% dplyr::filter(group ==group2) )$absLog2FC
# wil <- wilcox.test(distance1, distance2)
# return(wil$p.value)
# }
# convPvalue(getPvalWilcox(diff.RNA, \withBoundary\, \noBoundary\))
#
# Create the CDF plot
p <- ggplot(diff.RNA, aes(x = absLog2FC, color = group)) +
scale_color_manual(values = (c(\#777777\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
fileName <- paste0(\log2FC_cdf_insulationBoundary_convSS_pe\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Processing
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\ntemp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\\P-E\\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))\nggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = hasSS), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\PE\\) + geom_hline(yintercept = 0)\n\n\n\n\n# Counting loop types\nallLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%\n  dplyr::filter(!(loopID == loopID2))\ntemp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)\nresult <- temp %>%\n  group_by(loopID2, Anno2) %>%\n  summarise(count = n(), .groups = 'drop') %>%\n  group_by(loopID2) %>%\n  mutate(frequency = count / sum(count)) %>%\n  arrange(loopID2, Anno2)\n\nresult <- largest_loops %>% dplyr::left_join(result, by = c(\\loopID\\ = \\loopID2\\))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(result, by = c(\\gene\\))\ndata <- data %>% filter(complete.cases(.))\n\nall_anno2 <- unique(data$Anno2)\n\n# Calculate average frequency of Anno2 for each gene in each group\nresult <- data %>%\n  # Ensure all possible Anno2 values are present for each gene and group\n  complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>% \n  dplyr::mutate(group = case_when(\n    gene %in% gene.group1 ~ \\group1\\,\n    gene %in% gene.group2 ~ \\group2\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::select(group, Anno2, frequency)\n  \n\nggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()\n\n\n\n# View results\nggplot(largest_loops, aes(x = loop_width)) + geom_histogram()\n\n\n### Checking how many genes from each group has encompassing S-S\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n)\n\ndata_summary <- data %>%\n  group_by(group) %>%\n  summarize(\n    total = n(),\n    hasSS_yes = sum(hasSS == \\YES\\),\n    percentage_yes = (hasSS_yes / total) * 100\n  )\n\nggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +\n  geom_bar(stat = \\identity\\, color = \\black\\) +\n  labs(\n    title = \\Percentage of Each Group with hasSS = YES\\,\n    x = \\Group\\,\n    y = \\Percentage (%)\\\n  ) + ylim(0, 100) +\n  theme_minimal()\n\ntemp <- data %>% dplyr::filter(hasSS == \\YES\\)\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, \\group1\\, \\group2\\))\n\nggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\)\n\n\n### Comparing RNA perturbation\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(c(1, 4, 5)) %>%\n  dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \\hasSS\\, \\noSS\\))\n\n\nggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +\n  stat_ecdf(size = 0.4) +\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\n\n\n### Checking ChIP peak density\nlargest_loopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                            seqnames.field = \\loop_chr\\, \n                                            start.field = \\loop_start\\, \n                                            end.field = \\loop_end\\, \n                                            keep.extra.columns = TRUE)\n\n# Find overlaps between ChIP-seq peaks and the largest loops\nchip_overlaps <- findOverlaps(largest_loopsGr, peak.H3K4me3)\n\n# Count the number of ChIP-seq peaks per loop\nloop_peak_counts <- table(queryHits(chip_overlaps))\n\n# Create a data frame with the counts and loop details\nlargest_loops$density <- 0  # Initialize density column\nlargest_loops$count <- 0    # Initialize peak count column\n\n# Add peak counts to the corresponding loops\nlargest_loops[as.numeric(names(loop_peak_counts)), \\count\\] <- as.integer(loop_peak_counts)\n\n# Calculate the density (peaks per kilobase)\nlargest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)\n\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n) %>% dplyr::filter(hasSS == \\YES\\)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$density\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$density\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\npv <- convPvalue(getPvalWilcox(data, \\group1\\, \\group2\\))\n\nggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = group), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\H3K4me3\\)\n\n\n```\n\n<!-- rnb-source-end -->\n"} -->
temp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\P-E\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))
ggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = hasSS), fun = mean,
geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\) +
ggtitle(\PE\) + geom_hline(yintercept = 0)
# Counting loop types
allLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%
dplyr::filter(!(loopID == loopID2))
temp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)
result <- temp %>%
group_by(loopID2, Anno2) %>%
summarise(count = n(), .groups = 'drop') %>%
group_by(loopID2) %>%
mutate(frequency = count / sum(count)) %>%
arrange(loopID2, Anno2)
result <- largest_loops %>% dplyr::left_join(result, by = c(\loopID\ = \loopID2\))
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(result, by = c(\gene\))
data <- data %>% filter(complete.cases(.))
all_anno2 <- unique(data$Anno2)
# Calculate average frequency of Anno2 for each gene in each group
result <- data %>%
# Ensure all possible Anno2 values are present for each gene and group
complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>%
dplyr::mutate(group = case_when(
gene %in% gene.group1 ~ \group1\,
gene %in% gene.group2 ~ \group2\,
TRUE ~ NA
)) %>%
dplyr::select(group, Anno2, frequency)
ggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()
# View results
ggplot(largest_loops, aes(x = loop_width)) + geom_histogram()
### Checking how many genes from each group has encompassing S-S
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(largest_loops, by = c(\gene\))
data <- data %>% dplyr::mutate(
hasSS = ifelse(is.na(loop_width), \NO\, \YES\)
)
data_summary <- data %>%
group_by(group) %>%
summarize(
total = n(),
hasSS_yes = sum(hasSS == \YES\),
percentage_yes = (hasSS_yes / total) * 100
)
ggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +
geom_bar(stat = \identity\, color = \black\) +
labs(
title = \Percentage of Each Group with hasSS = YES\,
x = \Group\,
y = \Percentage (%)\
) + ylim(0, 100) +
theme_minimal()
temp <- data %>% dplyr::filter(hasSS == \YES\)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width
distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(temp, \group1\, \group2\))
ggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\)
### Comparing RNA perturbation
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(c(1, 4, 5)) %>%
dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \hasSS\, \noSS\))
ggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +
stat_ecdf(size = 0.4) +
labs(
x = \Absolute log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
### Checking ChIP peak density
largest_loopsGr <- makeGRangesFromDataFrame(largest_loops,
seqnames.field = \loop_chr\,
start.field = \loop_start\,
end.field = \loop_end\,
keep.extra.columns = TRUE)
# Find overlaps between ChIP-seq peaks and the largest loops
chip_overlaps <- findOverlaps(largest_loopsGr, peak.H3K4me3)
# Count the number of ChIP-seq peaks per loop
loop_peak_counts <- table(queryHits(chip_overlaps))
# Create a data frame with the counts and loop details
largest_loops$density <- 0 # Initialize density column
largest_loops$count <- 0 # Initialize peak count column
# Add peak counts to the corresponding loops
largest_loops[as.numeric(names(loop_peak_counts)), \count\] <- as.integer(loop_peak_counts)
# Calculate the density (peaks per kilobase)
largest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(largest_loops, by = c(\gene\))
data <- data %>% dplyr::mutate(
hasSS = ifelse(is.na(loop_width), \NO\, \YES\)
) %>% dplyr::filter(hasSS == \YES\)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$density
distance2 <- (data %>% dplyr::filter(group ==group2) )$density
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(data, \group1\, \group2\))
ggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\) +
ggtitle(\H3K4me3\)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ntemp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\\P-E\\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))\nggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = hasSS), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\PE\\) + geom_hline(yintercept = 0)\n\n\n\n\n# Counting loop types\nallLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%\n  dplyr::filter(!(loopID == loopID2))\ntemp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)\nresult <- temp %>%\n  group_by(loopID2, Anno2) %>%\n  summarise(count = n(), .groups = 'drop') %>%\n  group_by(loopID2) %>%\n  mutate(frequency = count / sum(count)) %>%\n  arrange(loopID2, Anno2)\n\nresult <- largest_loops %>% dplyr::left_join(result, by = c(\\loopID\\ = \\loopID2\\))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(result, by = c(\\gene\\))\ndata <- data %>% filter(complete.cases(.))\n\nall_anno2 <- unique(data$Anno2)\n\n# Calculate average frequency of Anno2 for each gene in each group\nresult <- data %>%\n  # Ensure all possible Anno2 values are present for each gene and group\n  complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>% \n  dplyr::mutate(group = case_when(\n    gene %in% gene.group1 ~ \\group1\\,\n    gene %in% gene.group2 ~ \\group2\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::select(group, Anno2, frequency)\n  \n\nggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()\n\n\n\n# View results\nggplot(largest_loops, aes(x = loop_width)) + geom_histogram()\n\n\n### Checking how many genes from each group has encompassing S-S\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n)\n\ndata_summary <- data %>%\n  group_by(group) %>%\n  summarize(\n    total = n(),\n    hasSS_yes = sum(hasSS == \\YES\\),\n    percentage_yes = (hasSS_yes / total) * 100\n  )\n\nggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +\n  geom_bar(stat = \\identity\\, color = \\black\\) +\n  labs(\n    title = \\Percentage of Each Group with hasSS = YES\\,\n    x = \\Group\\,\n    y = \\Percentage (%)\\\n  ) + ylim(0, 100) +\n  theme_minimal()\n\ntemp <- data %>% dplyr::filter(hasSS == \\YES\\)\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, \\group1\\, \\group2\\))\n\nggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\)\n\n\n### Comparing RNA perturbation\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(c(1, 4, 5)) %>%\n  dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \\hasSS\\, \\noSS\\))\n\n\nggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +\n  stat_ecdf(size = 0.4) +\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\n\n\n### Checking ChIP peak density\nlargest_loopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                            seqnames.field = \\loop_chr\\, \n                                            start.field = \\loop_start\\, \n                                            end.field = \\loop_end\\, \n                                            keep.extra.columns = TRUE)\n\n# Find overlaps between ChIP-seq peaks and the largest loops\nchip_overlaps <- findOverlaps(largest_loopsGr, peak.H3K4me3)\n\n# Count the number of ChIP-seq peaks per loop\nloop_peak_counts <- table(queryHits(chip_overlaps))\n\n# Create a data frame with the counts and loop details\nlargest_loops$density <- 0  # Initialize density column\nlargest_loops$count <- 0    # Initialize peak count column\n\n# Add peak counts to the corresponding loops\nlargest_loops[as.numeric(names(loop_peak_counts)), \\count\\] <- as.integer(loop_peak_counts)\n\n# Calculate the density (peaks per kilobase)\nlargest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)\n\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n) %>% dplyr::filter(hasSS == \\YES\\)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$density\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$density\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\npv <- convPvalue(getPvalWilcox(data, \\group1\\, \\group2\\))\n\nggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = group), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\H3K4me3\\)\n\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\ntemp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\\P-E\\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO\n  distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))\nggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = hasSS), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\PE\\) + geom_hline(yintercept = 0)\n\n\n\n\n# Counting loop types\nallLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%\n  dplyr::filter(!(loopID == loopID2))\ntemp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)\nresult <- temp %>%\n  group_by(loopID2, Anno2) %>%\n  summarise(count = n(), .groups = 'drop') %>%\n  group_by(loopID2) %>%\n  mutate(frequency = count / sum(count)) %>%\n  arrange(loopID2, Anno2)\n\nresult <- largest_loops %>% dplyr::left_join(result, by = c(\\loopID\\ = \\loopID2\\))\n\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(result, by = c(\\gene\\))\ndata <- data %>% filter(complete.cases(.))\n\nall_anno2 <- unique(data$Anno2)\n\n# Calculate average frequency of Anno2 for each gene in each group\nresult <- data %>%\n  # Ensure all possible Anno2 values are present for each gene and group\n  complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>% \n  dplyr::mutate(group = case_when(\n    gene %in% gene.group1 ~ \\group1\\,\n    gene %in% gene.group2 ~ \\group2\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::select(group, Anno2, frequency)\n  \n\nggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()\n\n\n\n# View results\nggplot(largest_loops, aes(x = loop_width)) + geom_histogram()\n\n\n### Checking how many genes from each group has encompassing S-S\ngene.group1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngene.group2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n)\n\ndata_summary <- data %>%\n  group_by(group) %>%\n  summarize(\n    total = n(),\n    hasSS_yes = sum(hasSS == \\YES\\),\n    percentage_yes = (hasSS_yes / total) * 100\n  )\n\nggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +\n  geom_bar(stat = \\identity\\, color = \\black\\) +\n  labs(\n    title = \\Percentage of Each Group with hasSS = YES\\,\n    x = \\Group\\,\n    y = \\Percentage (%)\\\n  ) + ylim(0, 100) +\n  theme_minimal()\n\ntemp <- data %>% dplyr::filter(hasSS == \\YES\\)\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\npv <- convPvalue(getPvalWilcox(temp, \\group1\\, \\group2\\))\n\nggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\)\n\n\n### Comparing RNA perturbation\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(c(1, 4, 5)) %>%\n  dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \\hasSS\\, \\noSS\\))\n\n\nggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +\n  stat_ecdf(size = 0.4) +\n  labs(\n    x = \\Absolute log2(fold change)\\,\n    y = \\Cumulative Probability\\\n  ) + coord_cartesian(xlim = c(0, 1.5)) +\n  theme_classic() + # Clean theme\n  theme(\n      axis.title = element_text(\n        size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n    )\n\n\n\n### Checking ChIP peak density\nlargest_loopsGr <- makeGRangesFromDataFrame(largest_loops, \n                                            seqnames.field = \\loop_chr\\, \n                                            start.field = \\loop_start\\, \n                                            end.field = \\loop_end\\, \n                                            keep.extra.columns = TRUE)\n\n# Find overlaps between ChIP-seq peaks and the largest loops\nchip_overlaps <- findOverlaps(largest_loopsGr, peak.H3K4me3)\n\n# Count the number of ChIP-seq peaks per loop\nloop_peak_counts <- table(queryHits(chip_overlaps))\n\n# Create a data frame with the counts and loop details\nlargest_loops$density <- 0  # Initialize density column\nlargest_loops$count <- 0    # Initialize peak count column\n\n# Add peak counts to the corresponding loops\nlargest_loops[as.numeric(names(loop_peak_counts)), \\count\\] <- as.integer(loop_peak_counts)\n\n# Calculate the density (peaks per kilobase)\nlargest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)\n\n\ndata <- tibble(\n  gene = c(gene.group1, gene.group2),\n  group = c(rep(\\group1\\, length(gene.group1)),\n            rep(\\group2\\, length(gene.group2)))\n)\n\ndata <- data %>% dplyr::left_join(largest_loops, by = c(\\gene\\))\ndata <- data %>% dplyr::mutate(\n  hasSS = ifelse(is.na(loop_width), \\NO\\, \\YES\\)\n) %>% dplyr::filter(hasSS == \\YES\\)\n\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(group ==group1) )$density\n  distance2 <- (data %>% dplyr::filter(group ==group2) )$density\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\npv <- convPvalue(getPvalWilcox(data, \\group1\\, \\group2\\))\n\nggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) + \n  geom_boxplot(width = 0.1, outlier.shape = NA) +\n  stat_summary(aes(group = group), fun = mean, \n               geom = \\point\\, shape = 21, size = 2, fill = \\red\\, color = \\black\\) +\n  theme_classic() +\n  annotate(\\text\\, x = 1, y = 0, label = pv, size = 5, color = \\black\\) +\n  ggtitle(\\H3K4me3\\)\n\n\n```\n```"} -->
```r
```r
temp <- allLoopsAnnotated %>% dplyr::filter(Anno2 %in% c(\P-E\)) %>% dplyr::mutate(hasSS = !is.na(loopID2))
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(hasSS ==group1) )$diff_dTAG_DMSO
distance2 <- (data %>% dplyr::filter(hasSS ==group2) )$diff_dTAG_DMSO
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(temp, TRUE, FALSE))
ggplot(temp, aes(x = hasSS, y = diff_dTAG_DMSO)) + geom_violin(aes(fill = hasSS), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = hasSS), fun = mean,
geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\) +
ggtitle(\PE\) + geom_hline(yintercept = 0)
# Counting loop types
allLoopsAnnotatedFiltered <- allLoopsAnnotated %>% dplyr::filter(!is.na(loopID2)) %>%
dplyr::filter(!(loopID == loopID2))
temp <- allLoopsAnnotatedFiltered %>% dplyr::select(loopID2, Anno2)
result <- temp %>%
group_by(loopID2, Anno2) %>%
summarise(count = n(), .groups = 'drop') %>%
group_by(loopID2) %>%
mutate(frequency = count / sum(count)) %>%
arrange(loopID2, Anno2)
result <- largest_loops %>% dplyr::left_join(result, by = c(\loopID\ = \loopID2\))
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(result, by = c(\gene\))
data <- data %>% filter(complete.cases(.))
all_anno2 <- unique(data$Anno2)
# Calculate average frequency of Anno2 for each gene in each group
result <- data %>%
# Ensure all possible Anno2 values are present for each gene and group
complete(gene, Anno2 = all_anno2, fill = list(frequency = 0)) %>%
dplyr::mutate(group = case_when(
gene %in% gene.group1 ~ \group1\,
gene %in% gene.group2 ~ \group2\,
TRUE ~ NA
)) %>%
dplyr::select(group, Anno2, frequency)
ggplot(result, aes(x = Anno2, fill = group, y = frequency)) + geom_boxplot()
# View results
ggplot(largest_loops, aes(x = loop_width)) + geom_histogram()
### Checking how many genes from each group has encompassing S-S
gene.group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
gene.group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(largest_loops, by = c(\gene\))
data <- data %>% dplyr::mutate(
hasSS = ifelse(is.na(loop_width), \NO\, \YES\)
)
data_summary <- data %>%
group_by(group) %>%
summarize(
total = n(),
hasSS_yes = sum(hasSS == \YES\),
percentage_yes = (hasSS_yes / total) * 100
)
ggplot(data_summary, aes(x = group, y = percentage_yes, fill = group)) +
geom_bar(stat = \identity\, color = \black\) +
labs(
title = \Percentage of Each Group with hasSS = YES\,
x = \Group\,
y = \Percentage (%)\
) + ylim(0, 100) +
theme_minimal()
temp <- data %>% dplyr::filter(hasSS == \YES\)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$loop_width
distance2 <- (data %>% dplyr::filter(group ==group2) )$loop_width
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(temp, \group1\, \group2\))
ggplot(temp, aes(x = group, y = loop_width)) + geom_violin() + geom_boxplot(width = 0.5) +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\)
### Comparing RNA perturbation
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(c(1, 4, 5)) %>%
dplyr::mutate(hasSS = ifelse(ensembl_gene_id %in% largest_loops$gene, \hasSS\, \noSS\))
ggplot(diff.RNA, aes(x = abs(log2FoldChange), color = hasSS)) +
stat_ecdf(size = 0.4) +
labs(
x = \Absolute log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)
### Checking ChIP peak density
largest_loopsGr <- makeGRangesFromDataFrame(largest_loops,
seqnames.field = \loop_chr\,
start.field = \loop_start\,
end.field = \loop_end\,
keep.extra.columns = TRUE)
# Find overlaps between ChIP-seq peaks and the largest loops
chip_overlaps <- findOverlaps(largest_loopsGr, peak.H3K4me3)
# Count the number of ChIP-seq peaks per loop
loop_peak_counts <- table(queryHits(chip_overlaps))
# Create a data frame with the counts and loop details
largest_loops$density <- 0 # Initialize density column
largest_loops$count <- 0 # Initialize peak count column
# Add peak counts to the corresponding loops
largest_loops[as.numeric(names(loop_peak_counts)), \count\] <- as.integer(loop_peak_counts)
# Calculate the density (peaks per kilobase)
largest_loops$density <- largest_loops$count / ((largest_loops$loop_width-1) / 1000)
data <- tibble(
gene = c(gene.group1, gene.group2),
group = c(rep(\group1\, length(gene.group1)),
rep(\group2\, length(gene.group2)))
)
data <- data %>% dplyr::left_join(largest_loops, by = c(\gene\))
data <- data %>% dplyr::mutate(
hasSS = ifelse(is.na(loop_width), \NO\, \YES\)
) %>% dplyr::filter(hasSS == \YES\)
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(group ==group1) )$density
distance2 <- (data %>% dplyr::filter(group ==group2) )$density
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
pv <- convPvalue(getPvalWilcox(data, \group1\, \group2\))
ggplot(data, aes(x = group, y = density)) + geom_violin(aes(fill = group), show.legend = FALSE) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
stat_summary(aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 2, fill = \red\, color = \black\) +
theme_classic() +
annotate(\text\, x = 1, y = 0, label = pv, size = 5, color = \black\) +
ggtitle(\H3K4me3\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Plotting
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nname <- \\chromo_cons_annoHierarchy\\\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n# UP NO\nloop.upno <- bind_rows(loop.up, loop.no)\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n\n\nallLoops <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>%\n  dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\)) %>%\n  dplyr::mutate(group = case_when(\n    loopID %in% loop.upno$loopID ~ \\UP&NO\\,\n    loopID %in% loop.down$loopID ~ \\DOWN\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group))\n\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\nallLoops <- allLoops %>% left_join(obsexp, by = c(\\id\\))\nallLoops$group <- factor(allLoops$group, levels = c(\\UP&NO\\, \\DOWN\\))\n\n\n### Plotting\np1 <- ggplot(allLoops, aes(x = group, y = diff_A485_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.5, 0.5))\n    \n\n####\np2 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_A485_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n  \n\n\n#############\n\n### Plotting\np3 <- ggplot(allLoops, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.5, 0.5))\n    \n\n####\np4 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n  \nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_dTAG\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p1, p2))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p1, p2))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_A485\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p3, p4))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p3, p4))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_loopScore\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p1, p3))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p1, p3))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_log2fc\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p2, p4))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p2, p4))\ndev.off()  \n\n```\n\n<!-- rnb-source-end -->\n"} -->
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(loopID = paste(V1, V2, V6, sep = \_\))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(loopID = paste(V1, V2, V6, sep = \_\))
# UP NO
loop.upno <- bind_rows(loop.up, loop.no)
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(loopID = paste(V1, V2, V6, sep = \_\))
allLoops <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>%
dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \_\)) %>%
dplyr::mutate(group = case_when(
loopID %in% loop.upno$loopID ~ \UP&NO\,
loopID %in% loop.down$loopID ~ \DOWN\,
TRUE ~ NA
)) %>%
dplyr::filter(!is.na(group))
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
allLoops <- allLoops %>% left_join(obsexp, by = c(\id\))
allLoops$group <- factor(allLoops$group, levels = c(\UP&NO\, \DOWN\))
### Plotting
p1 <- ggplot(allLoops, aes(x = group, y = diff_A485_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \grey50\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Δ loop score\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = -0.2,
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\) +
coord_cartesian(ylim = c(-0.5, 0.5))
####
p2 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_A485_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \grey50\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2(fc of obs/exp)\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
coord_cartesian(ylim = c(-2, 2))
#############
### Plotting
p3 <- ggplot(allLoops, aes(x = group, y = diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \grey50\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Δ loop score\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = -0.2,
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\) +
coord_cartesian(ylim = c(-0.5, 0.5))
####
p4 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \grey50\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2(fc of obs/exp)\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
coord_cartesian(ylim = c(-2, 2))
width <- panelSize(2)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\regLoopScorefromdTAG_dTAG\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(plot_grid(p1, p2))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(plot_grid(p1, p2))
dev.off()
width <- panelSize(2)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\regLoopScorefromdTAG_A485\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(plot_grid(p3, p4))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(plot_grid(p3, p4))
dev.off()
width <- panelSize(2)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\regLoopScorefromdTAG_loopScore\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(plot_grid(p1, p3))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(plot_grid(p1, p3))
dev.off()
width <- panelSize(2)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\regLoopScorefromdTAG_log2fc\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(plot_grid(p2, p4))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(plot_grid(p2, p4))
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n# UP NO\nloop.upno <- bind_rows(loop.up, loop.no)\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n\n\nallLoops <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>%\n  dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\)) %>%\n  dplyr::mutate(group = case_when(\n    loopID %in% loop.upno$loopID ~ \\UP&NO\\,\n    loopID %in% loop.down$loopID ~ \\DOWN\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group))\n\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\nallLoops <- allLoops %>% left_join(obsexp, by = c(\\id\\))\nallLoops$group <- factor(allLoops$group, levels = c(\\UP&NO\\, \\DOWN\\))\n\n\n### Plotting\np1 <- ggplot(allLoops, aes(x = group, y = diff_A485_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.5, 0.5))\n    \n\n####\np2 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_A485_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n  \n\n\n#############\n\n### Plotting\np3 <- ggplot(allLoops, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.5, 0.5))\n    \n\n####\np4 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n  \nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_dTAG\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p1, p2))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p1, p2))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_A485\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p3, p4))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p3, p4))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_loopScore\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p1, p3))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p1, p3))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_log2fc\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p2, p4))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p2, p4))\ndev.off()  \n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nname <- \\chromo_cons_annoHierarchy\\\n# UP loop\nloop.up <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n# NO loop\nloop.no <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n# UP NO\nloop.upno <- bind_rows(loop.up, loop.no)\n# DOWN loop\nloop.down <- fread(here(consensusDir, \\chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\\)) %>%\n  dplyr::mutate(loopID = paste(V1, V2, V6, sep = \\_\\))\n\n\nallLoops <- fread(here(consensusDir, \\chromo_cons_annoHierarchy.tsv\\)) %>%\n  dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \\_\\)) %>%\n  dplyr::mutate(group = case_when(\n    loopID %in% loop.upno$loopID ~ \\UP&NO\\,\n    loopID %in% loop.down$loopID ~ \\DOWN\\,\n    TRUE ~ NA\n  )) %>%\n  dplyr::filter(!is.na(group))\n\nminValue <- -4\nobsexp <- fread(here(consensusDir, paste0(\\loopScore_cons_obsexp.tsv\\))) %>%\n  dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),\n                log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),\n                log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),\n                log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,\n                log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)\n\nallLoops <- allLoops %>% left_join(obsexp, by = c(\\id\\))\nallLoops$group <- factor(allLoops$group, levels = c(\\UP&NO\\, \\DOWN\\))\n\n\n### Plotting\np1 <- ggplot(allLoops, aes(x = group, y = diff_A485_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.5, 0.5))\n    \n\n####\np2 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_A485_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n  \n\n\n#############\n\n### Plotting\np3 <- ggplot(allLoops, aes(x = group, y = diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\Δ loop score\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  geom_hline(yintercept = -0.2,\n             alpha = 0.5, \n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\, linetype = \\dashed\\) +\n  coord_cartesian(ylim = c(-0.5, 0.5))\n    \n\n####\np4 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) + \n  geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \\square\\, alpha = .4, , show.legend = FALSE) + \n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineThick * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA,  alpha = 0.6, show.legend = FALSE) + \n  theme_classic() + \n  stat_summary(\n    aes(group = group), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\, position = position_dodge(.3)\n  ) +\n  scale_fill_manual(values = c(\\grey50\\, \\grey50\\)) +\n\n  theme(\n    axis.title.x = element_blank(),\n    axis.title.y = element_text(\n      size = fontSizeM,\n      family = fontType,\n      color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS,\n      family = fontType,\n      color = \\#000000\\\n    ),    \n    axis.text.x = element_text(\n      size = fontSizeM,\n      angle = 45,      # Rotate x-axis labels 45 degrees\n      hjust = 1,       # Adjust horizontal justification\n      vjust = 1        # Adjust vertical justification\n    ),\n    axis.line = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\,\n      size = lineThick*mmToLineUnit,\n      lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +labs(y = \\log2(fc of obs/exp)\\)  +    \n  geom_hline(yintercept = 0,\n             alpha = 1,\n             color = \\black\\,\n             size = lineThick*mmToLineUnit,\n             lineend = \\square\\) + \n  coord_cartesian(ylim = c(-2, 2))\n  \nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_dTAG\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p1, p2))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p1, p2))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_A485\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p3, p4))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p3, p4))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_loopScore\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p1, p3))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p1, p3))\ndev.off()  \n\nwidth <- panelSize(2)*mmToInch\nheight <- panelSize(1.5)*mmToInch\nfileName <- paste0(\\regLoopScorefromdTAG_log2fc\\)\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(plot_grid(p2, p4))\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(plot_grid(p2, p4))\ndev.off()  \n\n```\n```"} -->
```r
```r
name <- \chromo_cons_annoHierarchy\
# UP loop
loop.up <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_UP_diff0.2.bedpe\)) %>%
dplyr::mutate(loopID = paste(V1, V2, V6, sep = \_\))
# NO loop
loop.no <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_NO_diff0.2.bedpe\)) %>%
dplyr::mutate(loopID = paste(V1, V2, V6, sep = \_\))
# UP NO
loop.upno <- bind_rows(loop.up, loop.no)
# DOWN loop
loop.down <- fread(here(consensusDir, \chromo_cons_annoHierarchy_pe-pe_dTAGvsDMSO_DOWN_diff0.2.bedpe\)) %>%
dplyr::mutate(loopID = paste(V1, V2, V6, sep = \_\))
allLoops <- fread(here(consensusDir, \chromo_cons_annoHierarchy.tsv\)) %>%
dplyr::mutate(loopID = paste(chrom1, start1, end2, sep = \_\)) %>%
dplyr::mutate(group = case_when(
loopID %in% loop.upno$loopID ~ \UP&NO\,
loopID %in% loop.down$loopID ~ \DOWN\,
TRUE ~ NA
)) %>%
dplyr::filter(!is.na(group))
minValue <- -4
obsexp <- fread(here(consensusDir, paste0(\loopScore_cons_obsexp.tsv\))) %>%
dplyr::mutate(log_obsexp_DMSO = if_else(obsexp_DMSO == 0, minValue, log2(obsexp_DMSO)),
log_obsexp_dTAG = if_else(obsexp_dTAG == 0, minValue, log2(obsexp_dTAG)),
log_obsexp_A485 = if_else(obsexp_A485 == 0, minValue, log2(obsexp_A485)),
log_obsexp_diff_dTAG_DMSO = log_obsexp_dTAG - log_obsexp_DMSO,
log_obsexp_diff_A485_DMSO = log_obsexp_A485 - log_obsexp_DMSO)
allLoops <- allLoops %>% left_join(obsexp, by = c(\id\))
allLoops$group <- factor(allLoops$group, levels = c(\UP&NO\, \DOWN\))
### Plotting
p1 <- ggplot(allLoops, aes(x = group, y = diff_A485_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \grey50\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Δ loop score\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = -0.2,
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\) +
coord_cartesian(ylim = c(-0.5, 0.5))
####
p2 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_A485_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \grey50\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2(fc of obs/exp)\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
coord_cartesian(ylim = c(-2, 2))
#############
### Plotting
p3 <- ggplot(allLoops, aes(x = group, y = diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \grey50\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \Δ loop score\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
geom_hline(yintercept = -0.2,
alpha = 0.5,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\, linetype = \dashed\) +
coord_cartesian(ylim = c(-0.5, 0.5))
####
p4 <- ggplot(allLoops, aes(x = group, y = log_obsexp_diff_dTAG_DMSO, fill = group)) +
geom_violin(linewidth = lineThick * mmToLineUnit, lineend = \square\, alpha = .4, , show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineThick * mmToLineUnit, lineend = \square\,
outlier.shape = NA, alpha = 0.6, show.legend = FALSE) +
theme_classic() +
stat_summary(
aes(group = group), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\, position = position_dodge(.3)
) +
scale_fill_manual(values = c(\grey50\, \grey50\)) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.text.x = element_text(
size = fontSizeM,
angle = 45, # Rotate x-axis labels 45 degrees
hjust = 1, # Adjust horizontal justification
vjust = 1 # Adjust vertical justification
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +labs(y = \log2(fc of obs/exp)\) +
geom_hline(yintercept = 0,
alpha = 1,
color = \black\,
size = lineThick*mmToLineUnit,
lineend = \square\) +
coord_cartesian(ylim = c(-2, 2))
width <- panelSize(2)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\regLoopScorefromdTAG_dTAG\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(plot_grid(p1, p2))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(plot_grid(p1, p2))
dev.off()
width <- panelSize(2)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\regLoopScorefromdTAG_A485\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(plot_grid(p3, p4))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(plot_grid(p3, p4))
dev.off()
width <- panelSize(2)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\regLoopScorefromdTAG_loopScore\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(plot_grid(p1, p3))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(plot_grid(p1, p3))
dev.off()
width <- panelSize(2)*mmToInch
height <- panelSize(1.5)*mmToInch
fileName <- paste0(\regLoopScorefromdTAG_log2fc\)
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(plot_grid(p2, p4))
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(plot_grid(p2, p4))
dev.off()
```
```
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n\npeak.RAD21 <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n\n##########################################################\nflankSize <- 5000\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V6, V5, V1, TSSstart, TSSend)\ncolnames(gene.tb) <- c(\\ensembl_gene_id\\, \\external_gene_name\\, \\chr\\, \\start\\, \\end\\)\n\n\n# Convert gene.tb to a GRanges object\ngene_gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$start, end = gene.tb$end),\n  gene_id = gene.tb$ensembl_gene_id\n)\n\n# Count overlaps between peaks and genes\noverlap_counts <- countOverlaps(gene_gr, peak.RAD21)\n\n# Add overlap counts to the original gene.tb data\ngene.tb$peak_count <- overlap_counts\n\n###############################################################################\n\npeakNum0 <- (gene.tb %>% dplyr::filter(peak_count == 0))$ensembl_gene_id\npeakNum1 <- (gene.tb %>% dplyr::filter(peak_count == 1))$ensembl_gene_id\npeakNum2 <- (gene.tb %>% dplyr::filter(peak_count == 2))$ensembl_gene_id\npeakNum3 <- (gene.tb %>% dplyr::filter(peak_count == 3))$ensembl_gene_id\npeakNumOver4 <- (gene.tb %>% dplyr::filter(peak_count >= 4))$ensembl_gene_id\n\n\n###############################################################################\nname <- \\chromo_cons_annoHierarchy\\\n\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nmaxLog2FC = 2\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \\2DOWN\\, \\0NO\\),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag) %>%\n  drop_na(shrinked_log2FC)\n\ntemp <- temp %>% dplyr::mutate(\n  pnOver = ifelse(gene %in% peakNum0, \\peakNum0\\,\n                  ifelse(gene %in% peakNum1, \\peakNum1\\,\n                         ifelse(gene %in% peakNum2, \\peakNum2\\,\n                                ifelse(gene %in% peakNum3, \\peakNum3\\,\n                                       ifelse(gene %in% peakNumOver4, \\peakNumOver4\\, NA)))))) %>%\n  drop_na(pnOver)\n\n###############################################################################\n# Checking percentage of genes with RAD21 peaks\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\naaa <- temp %>% dplyr::mutate(group = case_when(\n  gene %in% group1 ~ \\group1\\,\n  gene %in% group2 ~ \\group2\\,\n  TRUE ~ NA\n)) %>%\n  dplyr::select(group, pnOver, gene) %>%\n  dplyr::filter(group %in% c(\\group1\\, \\group2\\)) %>%\n  dplyr::mutate(hasPeak = ifelse(pnOver == \\peakNum0\\, FALSE, TRUE))\n\nbbb <- aaa %>% dplyr::filter(group == \\group1\\) \n\nsum(bbb$hasPeak)\nbbb <- aaa %>% dplyr::filter(group == \\group2\\) \nsum(bbb$hasPeak)\n\naaa_summary <- aaa %>%\n  group_by(group, pnOver) %>%\n  summarize(count = n(), .groups = \\drop\\)\n\n# Create the stacked barplot\naaa_summary <- aaa %>%\n  group_by(group, pnOver) %>%\n  summarize(count = n(), .groups = \\drop\\) %>%\n  group_by(group) %>%\n  mutate(ratio = count / sum(count))\n\n# Define the gradient colors\ngradient_colors <- c(\\#D4D4D4\\, \\#DCB0AF\\, \\#E48D8A\\, \\#EC6965\\, \\#F44641\\)\n# gradient_colors <- c(\\#D4D4D4\\, \\#A2BBCA\\, \\#71A2C0\\, \\#3F89B7\\, \\#0E71AD\\)\n\n# Create the stacked barplot with gradient colors\np <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = pnOver)) +\n  geom_bar(stat = \\identity\\, position = \\stack\\,\n           linewidth = lineMedium * mmToLineUnit, lineend = \\square\\) +\n  theme_classic() + labs(x = NULL , y = \\Ratio\\) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +\n  scale_fill_manual(values = gradient_colors, \n                    guide = guide_legend(\n      override.aes = list(size = 0.5), # Adjust legend key symbol size\n      keywidth = 2 / 2.54, # Convert 2mm to cm\n      keyheight = 2 / 2.54 # Convert 2mm to cm\n    )) \n\nwidth <- panelSize(1.7)*mmToInch\nheight <- panelSize(1.3)*mmToInch\nfileName <- paste0(\\rad21perc_promoter\\)\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n```\n\n<!-- rnb-source-end -->\n"} -->
peak.RAD21 <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
##########################################################
flankSize <- 5000
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V6, V5, V1, TSSstart, TSSend)
colnames(gene.tb) <- c(\ensembl_gene_id\, \external_gene_name\, \chr\, \start\, \end\)
# Convert gene.tb to a GRanges object
gene_gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$start, end = gene.tb$end),
gene_id = gene.tb$ensembl_gene_id
)
# Count overlaps between peaks and genes
overlap_counts <- countOverlaps(gene_gr, peak.RAD21)
# Add overlap counts to the original gene.tb data
gene.tb$peak_count <- overlap_counts
###############################################################################
peakNum0 <- (gene.tb %>% dplyr::filter(peak_count == 0))$ensembl_gene_id
peakNum1 <- (gene.tb %>% dplyr::filter(peak_count == 1))$ensembl_gene_id
peakNum2 <- (gene.tb %>% dplyr::filter(peak_count == 2))$ensembl_gene_id
peakNum3 <- (gene.tb %>% dplyr::filter(peak_count == 3))$ensembl_gene_id
peakNumOver4 <- (gene.tb %>% dplyr::filter(peak_count >= 4))$ensembl_gene_id
###############################################################################
name <- \chromo_cons_annoHierarchy\
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
pnOver = ifelse(gene %in% peakNum0, \peakNum0\,
ifelse(gene %in% peakNum1, \peakNum1\,
ifelse(gene %in% peakNum2, \peakNum2\,
ifelse(gene %in% peakNum3, \peakNum3\,
ifelse(gene %in% peakNumOver4, \peakNumOver4\, NA)))))) %>%
drop_na(pnOver)
###############################################################################
# Checking percentage of genes with RAD21 peaks
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
aaa <- temp %>% dplyr::mutate(group = case_when(
gene %in% group1 ~ \group1\,
gene %in% group2 ~ \group2\,
TRUE ~ NA
)) %>%
dplyr::select(group, pnOver, gene) %>%
dplyr::filter(group %in% c(\group1\, \group2\)) %>%
dplyr::mutate(hasPeak = ifelse(pnOver == \peakNum0\, FALSE, TRUE))
bbb <- aaa %>% dplyr::filter(group == \group1\)
sum(bbb$hasPeak)
bbb <- aaa %>% dplyr::filter(group == \group2\)
sum(bbb$hasPeak)
aaa_summary <- aaa %>%
group_by(group, pnOver) %>%
summarize(count = n(), .groups = \drop\)
# Create the stacked barplot
aaa_summary <- aaa %>%
group_by(group, pnOver) %>%
summarize(count = n(), .groups = \drop\) %>%
group_by(group) %>%
mutate(ratio = count / sum(count))
# Define the gradient colors
gradient_colors <- c(\#D4D4D4\, \#DCB0AF\, \#E48D8A\, \#EC6965\, \#F44641\)
# gradient_colors <- c(\#D4D4D4\, \#A2BBCA\, \#71A2C0\, \#3F89B7\, \#0E71AD\)
# Create the stacked barplot with gradient colors
p <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = pnOver)) +
geom_bar(stat = \identity\, position = \stack\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\) +
theme_classic() + labs(x = NULL , y = \Ratio\) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
scale_fill_manual(values = gradient_colors,
guide = guide_legend(
override.aes = list(size = 0.5), # Adjust legend key symbol size
keywidth = 2 / 2.54, # Convert 2mm to cm
keyheight = 2 / 2.54 # Convert 2mm to cm
))
width <- panelSize(1.7)*mmToInch
height <- panelSize(1.3)*mmToInch
fileName <- paste0(\rad21perc_promoter\)
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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QV9sb29wX2JpbmFyeUdyb3VwMi50c3ZcXCkpJGdlbmVcblxuYWFhIDwtIHRlbXAgJT4lIGRwbHlyOjptdXRhdGUoZ3JvdXAgPSBjYXNlX3doZW4oXG4gIGdlbmUgJWluJSBncm91cDEgfiBcXGdyb3VwMVxcLFxuICBnZW5lICVpbiUgZ3JvdXAyIH4gXFxncm91cDJcXCxcbiAgVFJVRSB+IE5BXG4pKSAlPiVcbiAgZHBseXI6OnNlbGVjdChncm91cCwgcG5PdmVyLCBnZW5lKSAlPiVcbiAgZHBseXI6OmZpbHRlcihncm91cCAlaW4lIGMoXFxncm91cDFcXCwgXFxncm91cDJcXCkpICU+JVxuICBkcGx5cjo6bXV0YXRlKGhhc1BlYWsgPSBpZmVsc2UocG5PdmVyID09IFxccGVha051bTBcXCwgRkFMU0UsIFRSVUUpKVxuXG5iYmIgPC0gYWFhICU+JSBkcGx5cjo6ZmlsdGVyKGdyb3VwID09IFxcZ3JvdXAxXFwpIFxuXG5zdW0oYmJiJGhhc1BlYWspXG5iYmIgPC0gYWFhICU+JSBkcGx5cjo6ZmlsdGVyKGdyb3VwID09IFxcZ3JvdXAyXFwpIFxuc3VtKGJiYiRoYXNQZWFrKVxuXG5hYWFfc3VtbWFyeSA8LSBhYWEgJT4lXG4gIGdyb3VwX2J5KGdyb3VwLCBwbk92ZXIpICU+JVxuICBzdW1tYXJpemUoY291bnQgPSBuKCksIC5ncm91cHMgPSBcXGRyb3BcXClcblxuIyBDcmVhdGUgdGhlIHN0YWNrZWQgYmFycGxvdFxuYWFhX3N1bW1hcnkgPC0gYWFhICU+JVxuICBncm91cF9ieShncm91cCwgcG5PdmVyKSAlPiVcbiAgc3VtbWFyaXplKGNvdW50ID0gbigpLCAuZ3JvdXBzID0gXFxkcm9wXFwpICU+JVxuICBncm91cF9ieShncm91cCkgJT4lXG4gIG11dGF0ZShyYXRpbyA9IGNvdW50IC8gc3VtKGNvdW50KSlcblxuIyBEZWZpbmUgdGhlIGdyYWRpZW50IGNvbG9yc1xuZ3JhZGllbnRfY29sb3JzIDwtIGMoXFwjRDRENEQ0XFwsIFxcI0RDQjBBRlxcLCBcXCNFNDhEOEFcXCwgXFwjRUM2OTY1XFwsIFxcI0Y0NDY0MVxcKVxuIyBncmFkaWVudF9jb2xvcnMgPC0gYyhcXCNENEQ0RDRcXCwgXFwjQTJCQkNBXFwsIFxcIzcxQTJDMFxcLCBcXCMzRjg5QjdcXCwgXFwjMEU3MUFEXFwpXG5cbiMgQ3JlYXRlIHRoZSBzdGFja2VkIGJhcnBsb3Qgd2l0aCBncmFkaWVudCBjb2xvcnNcbnAgPC0gZ2dwbG90KGFhYV9zdW1tYXJ5LCBhZXMoeCA9IGdyb3VwLCB5ID0gcmF0aW8sIGZpbGwgPSBwbk92ZXIpKSArXG4gIGdlb21fYmFyKHN0YXQgPSBcXGlkZW50aXR5XFwsIHBvc2l0aW9uID0gXFxzdGFja1xcLFxuICAgICAgICAgICBsaW5ld2lkdGggPSBsaW5lTWVkaXVtICogbW1Ub0xpbmVVbml0LCBsaW5lZW5kID0gXFxzcXVhcmVcXCkgK1xuICB0aGVtZV9jbGFzc2ljKCkgKyBsYWJzKHggPSBOVUxMICwgeSA9IFxcUmF0aW9cXCkgK1xuICB0aGVtZShcbiAgICBheGlzLnRpdGxlID0gZWxlbWVudF90ZXh0KFxuICAgICAgc2l6ZSA9IGZvbnRTaXplTSwgZmFtaWx5ID0gZm9udFR5cGUsIGNvbG9yID0gXFwjMDAwMDAwXFxcbiAgICApLFxuICAgIGF4aXMudGV4dCA9IGVsZW1lbnRfdGV4dChcbiAgICAgIHNpemUgPSBmb250U2l6ZVMsIGZhbWlseSA9IGZvbnRUeXBlLCBjb2xvciA9IFxcIzAwMDAwMFxcXG4gICAgKSxcbiAgICBheGlzLmxpbmUgPSBlbGVtZW50X2xpbmUoXG4gICAgICBjb2xvciA9IFxcIzAwMDAwMFxcLCBzaXplID0gbGluZVRoaWNrICogbW1Ub0xpbmVVbml0LCBsaW5lZW5kID0gXFxzcXVhcmVcXFxuICAgICksXG4gICAgYXhpcy50aWNrcyA9IGVsZW1lbnRfbGluZShcbiAgICAgIGNvbG9yID0gXFwjMDAwMDAwXFwsIHNpemUgPSBsaW5lVGhpY2sgKiBtbVRvTGluZVVuaXQsIGxpbmVlbmQgPSBcXHNxdWFyZVxcXG4gICAgKSxcbiAgICBwYW5lbC5iYWNrZ3JvdW5kID0gZWxlbWVudF9yZWN0KGZpbGwgPSBcXHRyYW5zcGFyZW50XFwpLFxuICAgIGxlZ2VuZC50ZXh0ID0gZWxlbWVudF90ZXh0KGZhbWlseSA9IGZvbnRUeXBlLCBzaXplID0gZm9udFNpemVTKSxcbiAgICBsZWdlbmQudGl0bGUgPSBlbGVtZW50X3RleHQoZmFtaWx5ID0gZm9udFR5cGUsIHNpemUgPSBmb250U2l6ZVMpXG4gICkgK1xuICBzY2FsZV9maWxsX21hbnVhbCh2YWx1ZXMgPSBncmFkaWVudF9jb2xvcnMsIFxuICAgICAgICAgICAgICAgICAgICBndWlkZSA9IGd1aWRlX2xlZ2VuZChcbiAgICAgIG92ZXJyaWRlLmFlcyA9IGxpc3Qoc2l6ZSA9IDAuNSksICMgQWRqdXN0IGxlZ2VuZCBrZXkgc3ltYm9sIHNpemVcbiAgICAgIGtleXdpZHRoID0gMiAvIDIuNTQsICMgQ29udmVydCAybW0gdG8gY21cbiAgICAgIGtleWhlaWdodCA9IDIgLyAyLjU0ICMgQ29udmVydCAybW0gdG8gY21cbiAgICApKSBcblxud2lkdGggPC0gcGFuZWxTaXplKDEuNykqbW1Ub0luY2hcbmhlaWdodCA8LSBwYW5lbFNpemUoMS4zKSptbVRvSW5jaFxuZmlsZU5hbWUgPC0gcGFzdGUwKFxccmFkMjFwZXJjX3Byb21vdGVyXFwpXG5zdmdsaXRlKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnN2Z1xcKSksICBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG5wcmludChwKVxuZGV2Lm9mZigpICBcblxuYGBgXG5gYGAifQ== -->\n\n```r\n```r\n\npeak.RAD21 <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n\n##########################################################\nflankSize <- 5000\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V6, V5, V1, TSSstart, TSSend)\ncolnames(gene.tb) <- c(\\ensembl_gene_id\\, \\external_gene_name\\, \\chr\\, \\start\\, \\end\\)\n\n\n# Convert gene.tb to a GRanges object\ngene_gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$start, end = gene.tb$end),\n  gene_id = gene.tb$ensembl_gene_id\n)\n\n# Count overlaps between peaks and genes\noverlap_counts <- countOverlaps(gene_gr, peak.RAD21)\n\n# Add overlap counts to the original gene.tb data\ngene.tb$peak_count <- overlap_counts\n\n###############################################################################\n\npeakNum0 <- (gene.tb %>% dplyr::filter(peak_count == 0))$ensembl_gene_id\npeakNum1 <- (gene.tb %>% dplyr::filter(peak_count == 1))$ensembl_gene_id\npeakNum2 <- (gene.tb %>% dplyr::filter(peak_count == 2))$ensembl_gene_id\npeakNum3 <- (gene.tb %>% dplyr::filter(peak_count == 3))$ensembl_gene_id\npeakNumOver4 <- (gene.tb %>% dplyr::filter(peak_count >= 4))$ensembl_gene_id\n\n\n###############################################################################\nname <- \\chromo_cons_annoHierarchy\\\n\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nmaxLog2FC = 2\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \\2DOWN\\, \\0NO\\),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag) %>%\n  drop_na(shrinked_log2FC)\n\ntemp <- temp %>% dplyr::mutate(\n  pnOver = ifelse(gene %in% peakNum0, \\peakNum0\\,\n                  ifelse(gene %in% peakNum1, \\peakNum1\\,\n                         ifelse(gene %in% peakNum2, \\peakNum2\\,\n                                ifelse(gene %in% peakNum3, \\peakNum3\\,\n                                       ifelse(gene %in% peakNumOver4, \\peakNumOver4\\, NA)))))) %>%\n  drop_na(pnOver)\n\n###############################################################################\n# Checking percentage of genes with RAD21 peaks\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\naaa <- temp %>% dplyr::mutate(group = case_when(\n  gene %in% group1 ~ \\group1\\,\n  gene %in% group2 ~ \\group2\\,\n  TRUE ~ NA\n)) %>%\n  dplyr::select(group, pnOver, gene) %>%\n  dplyr::filter(group %in% c(\\group1\\, \\group2\\)) %>%\n  dplyr::mutate(hasPeak = ifelse(pnOver == \\peakNum0\\, FALSE, TRUE))\n\nbbb <- aaa %>% dplyr::filter(group == \\group1\\) \n\nsum(bbb$hasPeak)\nbbb <- aaa %>% dplyr::filter(group == \\group2\\) \nsum(bbb$hasPeak)\n\naaa_summary <- aaa %>%\n  group_by(group, pnOver) %>%\n  summarize(count = n(), .groups = \\drop\\)\n\n# Create the stacked barplot\naaa_summary <- aaa %>%\n  group_by(group, pnOver) %>%\n  summarize(count = n(), .groups = \\drop\\) %>%\n  group_by(group) %>%\n  mutate(ratio = count / sum(count))\n\n# Define the gradient colors\ngradient_colors <- c(\\#D4D4D4\\, \\#DCB0AF\\, \\#E48D8A\\, \\#EC6965\\, \\#F44641\\)\n# gradient_colors <- c(\\#D4D4D4\\, \\#A2BBCA\\, \\#71A2C0\\, \\#3F89B7\\, \\#0E71AD\\)\n\n# Create the stacked barplot with gradient colors\np <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = pnOver)) +\n  geom_bar(stat = \\identity\\, position = \\stack\\,\n           linewidth = lineMedium * mmToLineUnit, lineend = \\square\\) +\n  theme_classic() + labs(x = NULL , y = \\Ratio\\) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +\n  scale_fill_manual(values = gradient_colors, \n                    guide = guide_legend(\n      override.aes = list(size = 0.5), # Adjust legend key symbol size\n      keywidth = 2 / 2.54, # Convert 2mm to cm\n      keyheight = 2 / 2.54 # Convert 2mm to cm\n    )) \n\nwidth <- panelSize(1.7)*mmToInch\nheight <- panelSize(1.3)*mmToInch\nfileName <- paste0(\\rad21perc_promoter\\)\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\n\npeak.RAD21 <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\n\n##########################################################\nflankSize <- 5000\ngene.tb <- fread(here(refDir, \\mm10_GRCm38.p6_gene_sorted.bed\\)) %>%\n  dplyr::mutate(TSS = ifelse(V4 == \\+\\, V2, V3),\n                TSSstart = TSS - flankSize,\n                TSSend = TSS + flankSize) %>%\n  dplyr::select(V6, V5, V1, TSSstart, TSSend)\ncolnames(gene.tb) <- c(\\ensembl_gene_id\\, \\external_gene_name\\, \\chr\\, \\start\\, \\end\\)\n\n\n# Convert gene.tb to a GRanges object\ngene_gr <- GRanges(\n  seqnames = gene.tb$chr,\n  ranges = IRanges(start = gene.tb$start, end = gene.tb$end),\n  gene_id = gene.tb$ensembl_gene_id\n)\n\n# Count overlaps between peaks and genes\noverlap_counts <- countOverlaps(gene_gr, peak.RAD21)\n\n# Add overlap counts to the original gene.tb data\ngene.tb$peak_count <- overlap_counts\n\n###############################################################################\n\npeakNum0 <- (gene.tb %>% dplyr::filter(peak_count == 0))$ensembl_gene_id\npeakNum1 <- (gene.tb %>% dplyr::filter(peak_count == 1))$ensembl_gene_id\npeakNum2 <- (gene.tb %>% dplyr::filter(peak_count == 2))$ensembl_gene_id\npeakNum3 <- (gene.tb %>% dplyr::filter(peak_count == 3))$ensembl_gene_id\npeakNumOver4 <- (gene.tb %>% dplyr::filter(peak_count >= 4))$ensembl_gene_id\n\n\n###############################################################################\nname <- \\chromo_cons_annoHierarchy\\\n\nalpha <- 0.05\nfcCutoff <- 0.5\ndiff.RNA.G1.dTAG <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)\ndiff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::filter(padj < alpha)\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-P\\, \\P-E\\, \\P-S\\, \\P-X\\))%>% \n  dplyr::mutate(distance = start2 - start1,\n                peakID = paste(chrom1, start1, start2, sep = \\_\\))\n\n\n## Dividing genes into groups\ntemp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>% \n  unnest(gene) %>% group_by(gene) %>%\n  summarize(mean_diff_score = mean(diff_dTAG_DMSO),\n            mean_distance = mean(distance),\n            .groups = 'drop')\n\ndiff.RNA <- fread(here(refDir, \\diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\\)) %>%\n  dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)\n\nmaxLog2FC = 2\n\ntemp <- left_join(temp, diff.RNA, by = c(\\gene\\ = \\ensembl_gene_id\\)) %>% \n  dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \\2DOWN\\, \\0NO\\),\n                maxFlag = (abs(shrinked_log2FC) > maxLog2FC),\n                log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>% \n  dplyr::arrange(flag) %>%\n  drop_na(shrinked_log2FC)\n\ntemp <- temp %>% dplyr::mutate(\n  pnOver = ifelse(gene %in% peakNum0, \\peakNum0\\,\n                  ifelse(gene %in% peakNum1, \\peakNum1\\,\n                         ifelse(gene %in% peakNum2, \\peakNum2\\,\n                                ifelse(gene %in% peakNum3, \\peakNum3\\,\n                                       ifelse(gene %in% peakNumOver4, \\peakNumOver4\\, NA)))))) %>%\n  drop_na(pnOver)\n\n###############################################################################\n# Checking percentage of genes with RAD21 peaks\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\naaa <- temp %>% dplyr::mutate(group = case_when(\n  gene %in% group1 ~ \\group1\\,\n  gene %in% group2 ~ \\group2\\,\n  TRUE ~ NA\n)) %>%\n  dplyr::select(group, pnOver, gene) %>%\n  dplyr::filter(group %in% c(\\group1\\, \\group2\\)) %>%\n  dplyr::mutate(hasPeak = ifelse(pnOver == \\peakNum0\\, FALSE, TRUE))\n\nbbb <- aaa %>% dplyr::filter(group == \\group1\\) \n\nsum(bbb$hasPeak)\nbbb <- aaa %>% dplyr::filter(group == \\group2\\) \nsum(bbb$hasPeak)\n\naaa_summary <- aaa %>%\n  group_by(group, pnOver) %>%\n  summarize(count = n(), .groups = \\drop\\)\n\n# Create the stacked barplot\naaa_summary <- aaa %>%\n  group_by(group, pnOver) %>%\n  summarize(count = n(), .groups = \\drop\\) %>%\n  group_by(group) %>%\n  mutate(ratio = count / sum(count))\n\n# Define the gradient colors\ngradient_colors <- c(\\#D4D4D4\\, \\#DCB0AF\\, \\#E48D8A\\, \\#EC6965\\, \\#F44641\\)\n# gradient_colors <- c(\\#D4D4D4\\, \\#A2BBCA\\, \\#71A2C0\\, \\#3F89B7\\, \\#0E71AD\\)\n\n# Create the stacked barplot with gradient colors\np <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = pnOver)) +\n  geom_bar(stat = \\identity\\, position = \\stack\\,\n           linewidth = lineMedium * mmToLineUnit, lineend = \\square\\) +\n  theme_classic() + labs(x = NULL , y = \\Ratio\\) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +\n  scale_fill_manual(values = gradient_colors, \n                    guide = guide_legend(\n      override.aes = list(size = 0.5), # Adjust legend key symbol size\n      keywidth = 2 / 2.54, # Convert 2mm to cm\n      keyheight = 2 / 2.54 # Convert 2mm to cm\n    )) \n\nwidth <- panelSize(1.7)*mmToInch\nheight <- panelSize(1.3)*mmToInch\nfileName <- paste0(\\rad21perc_promoter\\)\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n```\n```"} -->
```r
```r
peak.RAD21 <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
##########################################################
flankSize <- 5000
gene.tb <- fread(here(refDir, \mm10_GRCm38.p6_gene_sorted.bed\)) %>%
dplyr::mutate(TSS = ifelse(V4 == \+\, V2, V3),
TSSstart = TSS - flankSize,
TSSend = TSS + flankSize) %>%
dplyr::select(V6, V5, V1, TSSstart, TSSend)
colnames(gene.tb) <- c(\ensembl_gene_id\, \external_gene_name\, \chr\, \start\, \end\)
# Convert gene.tb to a GRanges object
gene_gr <- GRanges(
seqnames = gene.tb$chr,
ranges = IRanges(start = gene.tb$start, end = gene.tb$end),
gene_id = gene.tb$ensembl_gene_id
)
# Count overlaps between peaks and genes
overlap_counts <- countOverlaps(gene_gr, peak.RAD21)
# Add overlap counts to the original gene.tb data
gene.tb$peak_count <- overlap_counts
###############################################################################
peakNum0 <- (gene.tb %>% dplyr::filter(peak_count == 0))$ensembl_gene_id
peakNum1 <- (gene.tb %>% dplyr::filter(peak_count == 1))$ensembl_gene_id
peakNum2 <- (gene.tb %>% dplyr::filter(peak_count == 2))$ensembl_gene_id
peakNum3 <- (gene.tb %>% dplyr::filter(peak_count == 3))$ensembl_gene_id
peakNumOver4 <- (gene.tb %>% dplyr::filter(peak_count >= 4))$ensembl_gene_id
###############################################################################
name <- \chromo_cons_annoHierarchy\
alpha <- 0.05
fcCutoff <- 0.5
diff.RNA.G1.dTAG <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha, abs(shrinked_log2FC) > fcCutoff)
diff.RNA.G1.dTAG.noFCcutoff <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::filter(padj < alpha)
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-P\, \P-E\, \P-S\, \P-X\))%>%
dplyr::mutate(distance = start2 - start1,
peakID = paste(chrom1, start1, start2, sep = \_\))
## Dividing genes into groups
temp <- geneAnnoData %>% dplyr::select(diff_dTAG_DMSO, distance, gene) %>%
unnest(gene) %>% group_by(gene) %>%
summarize(mean_diff_score = mean(diff_dTAG_DMSO),
mean_distance = mean(distance),
.groups = 'drop')
diff.RNA <- fread(here(refDir, \diff_G1.dTAG_G1.2i.dTAG_vs_G1.2i.DMSO.tsv\)) %>%
dplyr::select(ensembl_gene_id, log2FoldChange, shrinked_log2FC, padj, external_gene_name)
maxLog2FC = 2
temp <- left_join(temp, diff.RNA, by = c(\gene\ = \ensembl_gene_id\)) %>%
dplyr::mutate(flag = ifelse(gene %in% diff.RNA.G1.dTAG$ensembl_gene_id, \2DOWN\, \0NO\),
maxFlag = (abs(shrinked_log2FC) > maxLog2FC),
log2fcMax = pmax(pmin(shrinked_log2FC, maxLog2FC), -maxLog2FC)) %>%
dplyr::arrange(flag) %>%
drop_na(shrinked_log2FC)
temp <- temp %>% dplyr::mutate(
pnOver = ifelse(gene %in% peakNum0, \peakNum0\,
ifelse(gene %in% peakNum1, \peakNum1\,
ifelse(gene %in% peakNum2, \peakNum2\,
ifelse(gene %in% peakNum3, \peakNum3\,
ifelse(gene %in% peakNumOver4, \peakNumOver4\, NA)))))) %>%
drop_na(pnOver)
###############################################################################
# Checking percentage of genes with RAD21 peaks
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
aaa <- temp %>% dplyr::mutate(group = case_when(
gene %in% group1 ~ \group1\,
gene %in% group2 ~ \group2\,
TRUE ~ NA
)) %>%
dplyr::select(group, pnOver, gene) %>%
dplyr::filter(group %in% c(\group1\, \group2\)) %>%
dplyr::mutate(hasPeak = ifelse(pnOver == \peakNum0\, FALSE, TRUE))
bbb <- aaa %>% dplyr::filter(group == \group1\)
sum(bbb$hasPeak)
bbb <- aaa %>% dplyr::filter(group == \group2\)
sum(bbb$hasPeak)
aaa_summary <- aaa %>%
group_by(group, pnOver) %>%
summarize(count = n(), .groups = \drop\)
# Create the stacked barplot
aaa_summary <- aaa %>%
group_by(group, pnOver) %>%
summarize(count = n(), .groups = \drop\) %>%
group_by(group) %>%
mutate(ratio = count / sum(count))
# Define the gradient colors
gradient_colors <- c(\#D4D4D4\, \#DCB0AF\, \#E48D8A\, \#EC6965\, \#F44641\)
# gradient_colors <- c(\#D4D4D4\, \#A2BBCA\, \#71A2C0\, \#3F89B7\, \#0E71AD\)
# Create the stacked barplot with gradient colors
p <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = pnOver)) +
geom_bar(stat = \identity\, position = \stack\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\) +
theme_classic() + labs(x = NULL , y = \Ratio\) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
scale_fill_manual(values = gradient_colors,
guide = guide_legend(
override.aes = list(size = 0.5), # Adjust legend key symbol size
keywidth = 2 / 2.54, # Convert 2mm to cm
keyheight = 2 / 2.54 # Convert 2mm to cm
))
width <- panelSize(1.7)*mmToInch
height <- panelSize(1.3)*mmToInch
fileName <- paste0(\rad21perc_promoter\)
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### [3.42] Comparing to Reg loops to A485
<!-- rnb-text-end -->
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-->\n\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$mean_diff_score\n  distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$mean_diff_score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nps01 <- round(getPvalWilcox(temp, \\peakNum0\\, \\peakNum1\\), 5)\nps12 <- round(getPvalWilcox(temp, \\peakNum1\\, \\peakNum2\\), 5)\nps23 <- round(getPvalWilcox(temp, \\peakNum2\\, \\peakNum3\\), 5)\nps34 <- round(getPvalWilcox(temp, \\peakNum3\\, \\peakNumOver4\\), 5)\nps24 <- round(getPvalWilcox(temp, \\peakNum2\\, \\peakNumOver4\\), 5)\nps14 <- round(getPvalWilcox(temp, \\peakNum1\\, \\peakNumOver4\\), 5)\nps04 <- round(getPvalWilcox(temp, \\peakNum0\\, \\peakNumOver4\\), 5)\n\n\np <- ggplot(temp, aes(x = pnOver, y = mean_diff_score)) + \n  geom_violin(aes(fill = pnOver), \n              color = \\black\\,\n              linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\Average Δ loop score\\) +\n  stat_summary(\n    aes(group = pnOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) +\n    scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  coord_cartesian(ylim = c(-0.5, 0.1))\n\n\n\nfileName <- paste0(\\diffScore_barplot_RAD21peakWithin10kb_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-38*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n\n<!-- rnb-source-end -->\n"} -->
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
ps01 <- round(getPvalWilcox(temp, \peakNum0\, \peakNum1\), 5)
ps12 <- round(getPvalWilcox(temp, \peakNum1\, \peakNum2\), 5)
ps23 <- round(getPvalWilcox(temp, \peakNum2\, \peakNum3\), 5)
ps34 <- round(getPvalWilcox(temp, \peakNum3\, \peakNumOver4\), 5)
ps24 <- round(getPvalWilcox(temp, \peakNum2\, \peakNumOver4\), 5)
ps14 <- round(getPvalWilcox(temp, \peakNum1\, \peakNumOver4\), 5)
ps04 <- round(getPvalWilcox(temp, \peakNum0\, \peakNumOver4\), 5)
p <- ggplot(temp, aes(x = pnOver, y = mean_diff_score)) +
geom_violin(aes(fill = pnOver),
color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \Average Δ loop score\) +
stat_summary(
aes(group = pnOver), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) +
scale_fill_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)) +
coord_cartesian(ylim = c(-0.5, 0.1))
fileName <- paste0(\diffScore_barplot_RAD21peakWithin10kb_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-38*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\ngetPvalWilcox <- function(data, group1, group2){\n  distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$mean_diff_score\n  distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$mean_diff_score\n  wil <- wilcox.test(distance1, distance2)\n  return(wil$p.value)\n}\n\nps01 <- round(getPvalWilcox(temp, \\peakNum0\\, \\peakNum1\\), 5)\nps12 <- round(getPvalWilcox(temp, \\peakNum1\\, \\peakNum2\\), 5)\nps23 <- round(getPvalWilcox(temp, \\peakNum2\\, \\peakNum3\\), 5)\nps34 <- round(getPvalWilcox(temp, \\peakNum3\\, \\peakNumOver4\\), 5)\nps24 <- round(getPvalWilcox(temp, \\peakNum2\\, \\peakNumOver4\\), 5)\nps14 <- round(getPvalWilcox(temp, \\peakNum1\\, \\peakNumOver4\\), 5)\nps04 <- round(getPvalWilcox(temp, \\peakNum0\\, \\peakNumOver4\\), 5)\n\n\np <- ggplot(temp, aes(x = pnOver, y = mean_diff_score)) + \n  geom_violin(aes(fill = pnOver), \n              color = \\black\\,\n              linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n              show.legend = FALSE) +\n  geom_boxplot(width = 0.3, color = \\black\\,\n               linewidth = lineMedium * mmToLineUnit, lineend = \\square\\,\n               outlier.shape = NA\n  ) + theme_classic() + labs(x = NULL , y = \\Average Δ loop score\\) +\n  stat_summary(\n    aes(group = pnOver), fun = mean,\n    geom = \\point\\, shape = 21, size = 0.5,\n    fill = \\black\\, color = \\black\\\n  ) +\n  geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.text.x = element_text(\n      angle = 45, hjust = 1, vjust = 1\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  )+\n  annotate(\\text\\, x = 1, y = 0, label = paste0(\\ps01: \\, convPvalue(ps01), \\\\n\\,\n                                                \\ps12: \\, convPvalue(ps12), \\\\n\\,\n                                                \\ps23: \\, convPvalue(ps23), \\\\n\\,\n                                                \\ps34: \\, convPvalue(ps34), \\\\n\\,\n                                                \\ps24: \\, convPvalue(ps24), \\\\n\\, \n                                                \\ps14: \\, convPvalue(ps14), \\\\n\\,\n                                                \\ps04: \\, convPvalue(ps04), \\\\n\\), \n           color = \\black\\, hjust = 0, size = 2) +\n    scale_fill_manual(values = c(\\#777777\\, \\#8B7E65\\, \\#A28452\\, \\#C2884D\\, \\#F28E2C\\)) +\n  coord_cartesian(ylim = c(-0.5, 0.1))\n\n\n\nfileName <- paste0(\\diffScore_barplot_RAD21peakWithin10kb_dTAG_vs_DMSO\\)\nwidth <- 33*mmToInch\nheight <-38*mmToInch\npng(here(figDir, paste0(fileName, \\.png\\)), res = 600, unit = \\in\\, height = height, width = width)\nprint(p)\ndev.off()\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
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```r
```r
getPvalWilcox <- function(data, group1, group2){
distance1 <- (data %>% dplyr::filter(pnOver ==group1) )$mean_diff_score
distance2 <- (data %>% dplyr::filter(pnOver ==group2) )$mean_diff_score
wil <- wilcox.test(distance1, distance2)
return(wil$p.value)
}
ps01 <- round(getPvalWilcox(temp, \peakNum0\, \peakNum1\), 5)
ps12 <- round(getPvalWilcox(temp, \peakNum1\, \peakNum2\), 5)
ps23 <- round(getPvalWilcox(temp, \peakNum2\, \peakNum3\), 5)
ps34 <- round(getPvalWilcox(temp, \peakNum3\, \peakNumOver4\), 5)
ps24 <- round(getPvalWilcox(temp, \peakNum2\, \peakNumOver4\), 5)
ps14 <- round(getPvalWilcox(temp, \peakNum1\, \peakNumOver4\), 5)
ps04 <- round(getPvalWilcox(temp, \peakNum0\, \peakNumOver4\), 5)
p <- ggplot(temp, aes(x = pnOver, y = mean_diff_score)) +
geom_violin(aes(fill = pnOver),
color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
show.legend = FALSE) +
geom_boxplot(width = 0.3, color = \black\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\,
outlier.shape = NA
) + theme_classic() + labs(x = NULL , y = \Average Δ loop score\) +
stat_summary(
aes(group = pnOver), fun = mean,
geom = \point\, shape = 21, size = 0.5,
fill = \black\, color = \black\
) +
geom_hline(yintercept = 0, linewidth = lineThick * mmToLineUnit) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.text.x = element_text(
angle = 45, hjust = 1, vjust = 1
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
)+
annotate(\text\, x = 1, y = 0, label = paste0(\ps01: \, convPvalue(ps01), \\n\,
\ps12: \, convPvalue(ps12), \\n\,
\ps23: \, convPvalue(ps23), \\n\,
\ps34: \, convPvalue(ps34), \\n\,
\ps24: \, convPvalue(ps24), \\n\,
\ps14: \, convPvalue(ps14), \\n\,
\ps04: \, convPvalue(ps04), \\n\),
color = \black\, hjust = 0, size = 2) +
scale_fill_manual(values = c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\)) +
coord_cartesian(ylim = c(-0.5, 0.1))
fileName <- paste0(\diffScore_barplot_RAD21peakWithin10kb_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-38*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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### [3.43] Checking RAD21 peak density at group1 and group2 promoter
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<!-- rnb-output-begin 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bmcoaGVyZShmaWdEaXIsIHBhc3RlMChmaWxlTmFtZSwgXFwucG5nXFwpKSwgcmVzID0gNjAwLCB1bml0ID0gXFxpblxcLCBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG5wcmludChwKVxuZGV2Lm9mZigpXG5zdmdsaXRlKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFxcLnN2Z1xcKSksICBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG5wcmludChwKVxuZGV2Lm9mZigpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
temp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))
p <- ggplot(temp, aes(x = absLog2FC, color = pnOver)) +
scale_color_manual(values = (c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\log2FC_cdf_psGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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```r
```r
temp <- temp %>% dplyr::mutate(absLog2FC = abs(log2FoldChange))
p <- ggplot(temp, aes(x = absLog2FC, color = pnOver)) +
scale_color_manual(values = (c(\#777777\, \#8B7E65\, \#A28452\, \#C2884D\, \#F28E2C\))) +
stat_ecdf(size = 0.4, linewidth = lineMedium * mmToLineUnit, lineend = \square\ ) + # Use stat_ecdf to plot the empirical CDF
labs(
x = \Abs. log2(fold change)\,
y = \Cumulative Probability\
) + coord_cartesian(xlim = c(0, 1.5)) +
theme_classic() + # Clean theme
theme(
axis.title = element_text(
size = fontSizeM,
family = fontType,
color = \#000000\
),
axis.text = element_text(
size = fontSizeS,
family = fontType,
color = \#000000\
),
axis.line = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
axis.ticks = element_line(
color = \#000000\,
size = lineThick*mmToLineUnit,
lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.position = \none\,
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))
fileName <- paste0(\log2FC_cdf_psGroup_dTAG_vs_DMSO\)
width <- 33*mmToInch
height <-33*mmToInch
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
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##### loop score
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-->\n\n```r\n\npeak.RAD21 <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\n##########################################################\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-E\\))\n\ntemp1 <- geneAnnoData %>% dplyr::filter(A1 == \\E\\) %>% dplyr::select(chrom1, start1, end1, gene)\ncolnames(temp1) <- c(\\chr\\, \\start\\, \\end\\, \\gene\\)\ntemp2 <- geneAnnoData %>% dplyr::filter(A2 == \\E\\) %>% dplyr::select(chrom2, start2, end2, gene)\ncolnames(temp2) <- c(\\chr\\, \\start\\, \\end\\, \\gene\\)\n\nenhAnchors <- bind_rows(temp1, temp2) %>% unnest(gene) %>%\n  dplyr::mutate(\n    group = case_when(\n      gene %in% group1 ~ \\Grp1\\,\n      gene %in% group2 ~ \\Grp2\\,\n      TRUE ~ NA\n    )\n  ) %>% dplyr::filter(!is.na(group))\n\nenh <- makeGRangesFromDataFrame(enhAnchors %>% dplyr::select(c(1, 2, 3)))\n\n# Count overlaps between peaks and genes\noverlap_counts <- countOverlaps(enh, peak.RAD21)\n\n# Add overlap counts to the original gene.tb data\nenhAnchors$peak_count <- overlap_counts\n\nenhAnchors <- enhAnchors %>% dplyr::mutate(\n  peakGroup = case_when(\n    peak_count == 0 ~ \\peakNum0\\,\n    peak_count == 1 ~ \\peakNum1\\,\n    peak_count == 2 ~ \\peakNum2\\,\n    peak_count == 3 ~ \\peakNum3\\,\n    peak_count >= 4 ~ \\peakNumOver4\\\n  )\n)\n\naaa <- enhAnchors %>% \n  dplyr::select(group, peakGroup, gene) %>%\n  dplyr::mutate(hasPeak = ifelse(peakGroup == \\peakNum0\\, FALSE, TRUE))\n\nbbb <- aaa %>% dplyr::filter(group == \\Grp1\\) \nsum(bbb$hasPeak)/nrow(bbb)\nbbb <- aaa %>% dplyr::filter(group == \\Grp2\\) \nsum(bbb$hasPeak)/nrow(bbb)\n\n\naaa_summary <- aaa %>%\n  group_by(group, peakGroup) %>%\n  summarize(count = n(), .groups = \\drop\\)\n\n# Create the stacked barplot\naaa_summary <- aaa %>%\n  group_by(group, peakGroup) %>%\n  summarize(count = n(), .groups = \\drop\\) %>%\n  group_by(group) %>%\n  mutate(ratio = count / sum(count))\n\n# Define the gradient colors\n# gradient_colors <- c(\\#D4D4D4\\, \\#DCB0AF\\, \\#E48D8A\\, \\#EC6965\\, \\#F44641\\)\ngradient_colors <- c(\\#D4D4D4\\, \\#A2BBCA\\, \\#71A2C0\\, \\#3F89B7\\, \\#0E71AD\\)\n\n# Create the stacked barplot with gradient colors\np <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = peakGroup)) +\n  geom_bar(stat = \\identity\\, position = \\stack\\,\n           linewidth = lineMedium * mmToLineUnit, lineend = \\square\\) +\n  theme_classic() + labs(x = NULL , y = \\Ratio\\) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +\n  scale_fill_manual(values = gradient_colors, \n                    guide = guide_legend(\n      override.aes = list(size = 0.5), # Adjust legend key symbol size\n      keywidth = 2 / 2.54, # Convert 2mm to cm\n      keyheight = 2 / 2.54 # Convert 2mm to cm\n    )) \n\nwidth <- panelSize(1.7)*mmToInch\nheight <- panelSize(1.3)*mmToInch\nfileName <- paste0(\\rad21perc_enh\\)\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n```\n\n<!-- rnb-source-end -->\n"} -->
peak.RAD21 <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
##########################################################
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-E\))
temp1 <- geneAnnoData %>% dplyr::filter(A1 == \E\) %>% dplyr::select(chrom1, start1, end1, gene)
colnames(temp1) <- c(\chr\, \start\, \end\, \gene\)
temp2 <- geneAnnoData %>% dplyr::filter(A2 == \E\) %>% dplyr::select(chrom2, start2, end2, gene)
colnames(temp2) <- c(\chr\, \start\, \end\, \gene\)
enhAnchors <- bind_rows(temp1, temp2) %>% unnest(gene) %>%
dplyr::mutate(
group = case_when(
gene %in% group1 ~ \Grp1\,
gene %in% group2 ~ \Grp2\,
TRUE ~ NA
)
) %>% dplyr::filter(!is.na(group))
enh <- makeGRangesFromDataFrame(enhAnchors %>% dplyr::select(c(1, 2, 3)))
# Count overlaps between peaks and genes
overlap_counts <- countOverlaps(enh, peak.RAD21)
# Add overlap counts to the original gene.tb data
enhAnchors$peak_count <- overlap_counts
enhAnchors <- enhAnchors %>% dplyr::mutate(
peakGroup = case_when(
peak_count == 0 ~ \peakNum0\,
peak_count == 1 ~ \peakNum1\,
peak_count == 2 ~ \peakNum2\,
peak_count == 3 ~ \peakNum3\,
peak_count >= 4 ~ \peakNumOver4\
)
)
aaa <- enhAnchors %>%
dplyr::select(group, peakGroup, gene) %>%
dplyr::mutate(hasPeak = ifelse(peakGroup == \peakNum0\, FALSE, TRUE))
bbb <- aaa %>% dplyr::filter(group == \Grp1\)
sum(bbb$hasPeak)/nrow(bbb)
bbb <- aaa %>% dplyr::filter(group == \Grp2\)
sum(bbb$hasPeak)/nrow(bbb)
aaa_summary <- aaa %>%
group_by(group, peakGroup) %>%
summarize(count = n(), .groups = \drop\)
# Create the stacked barplot
aaa_summary <- aaa %>%
group_by(group, peakGroup) %>%
summarize(count = n(), .groups = \drop\) %>%
group_by(group) %>%
mutate(ratio = count / sum(count))
# Define the gradient colors
# gradient_colors <- c(\#D4D4D4\, \#DCB0AF\, \#E48D8A\, \#EC6965\, \#F44641\)
gradient_colors <- c(\#D4D4D4\, \#A2BBCA\, \#71A2C0\, \#3F89B7\, \#0E71AD\)
# Create the stacked barplot with gradient colors
p <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = peakGroup)) +
geom_bar(stat = \identity\, position = \stack\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\) +
theme_classic() + labs(x = NULL , y = \Ratio\) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
scale_fill_manual(values = gradient_colors,
guide = guide_legend(
override.aes = list(size = 0.5), # Adjust legend key symbol size
keywidth = 2 / 2.54, # Convert 2mm to cm
keyheight = 2 / 2.54 # Convert 2mm to cm
))
width <- panelSize(1.7)*mmToInch
height <- panelSize(1.3)*mmToInch
fileName <- paste0(\rad21perc_enh\)
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
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-->\n\n```r\n```r\n\npeak.RAD21 <- importPeak(here(refDir, \\33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\\))\ngroup1 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\\))$gene\ngroup2 <- fread(here(refDir, \\geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\\))$gene\n\n\n##########################################################\nname <- \\chromo_cons_annoHierarchy\\\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \\_p-n_ensemblList.tsv\\)),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\\P-E\\))\n\ntemp1 <- geneAnnoData %>% dplyr::filter(A1 == \\E\\) %>% dplyr::select(chrom1, start1, end1, gene)\ncolnames(temp1) <- c(\\chr\\, \\start\\, \\end\\, \\gene\\)\ntemp2 <- geneAnnoData %>% dplyr::filter(A2 == \\E\\) %>% dplyr::select(chrom2, start2, end2, gene)\ncolnames(temp2) <- c(\\chr\\, \\start\\, \\end\\, \\gene\\)\n\nenhAnchors <- bind_rows(temp1, temp2) %>% unnest(gene) %>%\n  dplyr::mutate(\n    group = case_when(\n      gene %in% group1 ~ \\Grp1\\,\n      gene %in% group2 ~ \\Grp2\\,\n      TRUE ~ NA\n    )\n  ) %>% dplyr::filter(!is.na(group))\n\nenh <- makeGRangesFromDataFrame(enhAnchors %>% dplyr::select(c(1, 2, 3)))\n\n# Count overlaps between peaks and genes\noverlap_counts <- countOverlaps(enh, peak.RAD21)\n\n# Add overlap counts to the original gene.tb data\nenhAnchors$peak_count <- overlap_counts\n\nenhAnchors <- enhAnchors %>% dplyr::mutate(\n  peakGroup = case_when(\n    peak_count == 0 ~ \\peakNum0\\,\n    peak_count == 1 ~ \\peakNum1\\,\n    peak_count == 2 ~ \\peakNum2\\,\n    peak_count == 3 ~ \\peakNum3\\,\n    peak_count >= 4 ~ \\peakNumOver4\\\n  )\n)\n\naaa <- enhAnchors %>% \n  dplyr::select(group, peakGroup, gene) %>%\n  dplyr::mutate(hasPeak = ifelse(peakGroup == \\peakNum0\\, FALSE, TRUE))\n\nbbb <- aaa %>% dplyr::filter(group == \\Grp1\\) \nsum(bbb$hasPeak)/nrow(bbb)\nbbb <- aaa %>% dplyr::filter(group == \\Grp2\\) \nsum(bbb$hasPeak)/nrow(bbb)\n\n\naaa_summary <- aaa %>%\n  group_by(group, peakGroup) %>%\n  summarize(count = n(), .groups = \\drop\\)\n\n# Create the stacked barplot\naaa_summary <- aaa %>%\n  group_by(group, peakGroup) %>%\n  summarize(count = n(), .groups = \\drop\\) %>%\n  group_by(group) %>%\n  mutate(ratio = count / sum(count))\n\n# Define the gradient colors\n# gradient_colors <- c(\\#D4D4D4\\, \\#DCB0AF\\, \\#E48D8A\\, \\#EC6965\\, \\#F44641\\)\ngradient_colors <- c(\\#D4D4D4\\, \\#A2BBCA\\, \\#71A2C0\\, \\#3F89B7\\, \\#0E71AD\\)\n\n# Create the stacked barplot with gradient colors\np <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = peakGroup)) +\n  geom_bar(stat = \\identity\\, position = \\stack\\,\n           linewidth = lineMedium * mmToLineUnit, lineend = \\square\\) +\n  theme_classic() + labs(x = NULL , y = \\Ratio\\) +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \\#000000\\\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \\#000000\\\n    ),\n    axis.line = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    axis.ticks = element_line(\n      color = \\#000000\\, size = lineThick * mmToLineUnit, lineend = \\square\\\n    ),\n    panel.background = element_rect(fill = \\transparent\\),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +\n  scale_fill_manual(values = gradient_colors, \n                    guide = guide_legend(\n      override.aes = list(size = 0.5), # Adjust legend key symbol size\n      keywidth = 2 / 2.54, # Convert 2mm to cm\n      keyheight = 2 / 2.54 # Convert 2mm to cm\n    )) \n\nwidth <- panelSize(1.7)*mmToInch\nheight <- panelSize(1.3)*mmToInch\nfileName <- paste0(\\rad21perc_enh\\)\nsvglite(here(figDir, paste0(fileName, \\.svg\\)),  height = height, width = width)\nprint(p)\ndev.off()  \n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
peak.RAD21 <- importPeak(here(refDir, \33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\))
group1 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\))$gene
group2 <- fread(here(refDir, \geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\))$gene
##########################################################
name <- \chromo_cons_annoHierarchy\
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \_p-n_ensemblList.tsv\)),
diffCutoff = 0.2,
annoList = c(\P-E\))
temp1 <- geneAnnoData %>% dplyr::filter(A1 == \E\) %>% dplyr::select(chrom1, start1, end1, gene)
colnames(temp1) <- c(\chr\, \start\, \end\, \gene\)
temp2 <- geneAnnoData %>% dplyr::filter(A2 == \E\) %>% dplyr::select(chrom2, start2, end2, gene)
colnames(temp2) <- c(\chr\, \start\, \end\, \gene\)
enhAnchors <- bind_rows(temp1, temp2) %>% unnest(gene) %>%
dplyr::mutate(
group = case_when(
gene %in% group1 ~ \Grp1\,
gene %in% group2 ~ \Grp2\,
TRUE ~ NA
)
) %>% dplyr::filter(!is.na(group))
enh <- makeGRangesFromDataFrame(enhAnchors %>% dplyr::select(c(1, 2, 3)))
# Count overlaps between peaks and genes
overlap_counts <- countOverlaps(enh, peak.RAD21)
# Add overlap counts to the original gene.tb data
enhAnchors$peak_count <- overlap_counts
enhAnchors <- enhAnchors %>% dplyr::mutate(
peakGroup = case_when(
peak_count == 0 ~ \peakNum0\,
peak_count == 1 ~ \peakNum1\,
peak_count == 2 ~ \peakNum2\,
peak_count == 3 ~ \peakNum3\,
peak_count >= 4 ~ \peakNumOver4\
)
)
aaa <- enhAnchors %>%
dplyr::select(group, peakGroup, gene) %>%
dplyr::mutate(hasPeak = ifelse(peakGroup == \peakNum0\, FALSE, TRUE))
bbb <- aaa %>% dplyr::filter(group == \Grp1\)
sum(bbb$hasPeak)/nrow(bbb)
bbb <- aaa %>% dplyr::filter(group == \Grp2\)
sum(bbb$hasPeak)/nrow(bbb)
aaa_summary <- aaa %>%
group_by(group, peakGroup) %>%
summarize(count = n(), .groups = \drop\)
# Create the stacked barplot
aaa_summary <- aaa %>%
group_by(group, peakGroup) %>%
summarize(count = n(), .groups = \drop\) %>%
group_by(group) %>%
mutate(ratio = count / sum(count))
# Define the gradient colors
# gradient_colors <- c(\#D4D4D4\, \#DCB0AF\, \#E48D8A\, \#EC6965\, \#F44641\)
gradient_colors <- c(\#D4D4D4\, \#A2BBCA\, \#71A2C0\, \#3F89B7\, \#0E71AD\)
# Create the stacked barplot with gradient colors
p <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = peakGroup)) +
geom_bar(stat = \identity\, position = \stack\,
linewidth = lineMedium * mmToLineUnit, lineend = \square\) +
theme_classic() + labs(x = NULL , y = \Ratio\) +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = \#000000\
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = \#000000\
),
axis.line = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
axis.ticks = element_line(
color = \#000000\, size = lineThick * mmToLineUnit, lineend = \square\
),
panel.background = element_rect(fill = \transparent\),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
scale_fill_manual(values = gradient_colors,
guide = guide_legend(
override.aes = list(size = 0.5), # Adjust legend key symbol size
keywidth = 2 / 2.54, # Convert 2mm to cm
keyheight = 2 / 2.54 # Convert 2mm to cm
))
width <- panelSize(1.7)*mmToInch
height <- panelSize(1.3)*mmToInch
fileName <- paste0(\rad21perc_enh\)
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
##### log2FC
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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data <- fread(here(resultDir, \chromHMM\, \A485_pe-pe_anchors\, \overlap_enrich_100_state.txt\))
colnames(data) <- c(\state\, \genome\, \pe-pe_A485_down\, \pe-pe_A485_no\, \pe-pe_A485_up\)
data <- data %>% dplyr::select(-genome) %>% as.matrix()
column_to_rownames(var = \state\) %>%
as.matrix()
library(circlize)
col_fun <- colorRamp2(c(0, 1, 10),
c(\blue\, \white\, \red\))
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 10, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_regAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
````
```r
data <- fread(here(resultDir, \chromHMM\, \A485_pe-pe_anchors\, \overlap_enrich_100_state.txt\))
colnames(data) <- c(\state\, \genome\, \pe-pe_A485_down\, \pe-pe_A485_no\, \pe-pe_A485_up\)
data <- data %>% dplyr::select(-genome) %>% as.matrix()
column_to_rownames(var = \state\) %>%
as.matrix()
library(circlize)
col_fun <- colorRamp2(c(0, 1, 10),
c(\blue\, \white\, \red\))
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c(\white\, \red\))
#fviz_nbclust(data, kmeans, method = \wss\)
p <- Heatmap(
data,
name = \Odds Ratio\, # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 10, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
fileName <- paste0(\anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_regAnchorBackground_atac\)
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, \.png\)), res = 600, unit = \in\, height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, \.svg\)), height = height, width = width)
print(p)
dev.off()
<!-- rnb-source-end -->
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<!-- rnb-text-begin -->
### [3.44] Checking RAD21 peak density at group1 and group2 promoter
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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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bSB0byBjbVxuICAgICkpIFxuXG53aWR0aCA8LSBwYW5lbFNpemUoMS43KSptbVRvSW5jaFxuaGVpZ2h0IDwtIHBhbmVsU2l6ZSgxLjMpKm1tVG9JbmNoXG5maWxlTmFtZSA8LSBwYXN0ZTAoXCJyYWQyMXBlcmNfZW5oXCIpXG5zdmdsaXRlKGhlcmUoZmlnRGlyLCBwYXN0ZTAoZmlsZU5hbWUsIFwiLnN2Z1wiKSksICBoZWlnaHQgPSBoZWlnaHQsIHdpZHRoID0gd2lkdGgpXG5wcmludChwKVxuZGV2Lm9mZigpICBcblxuYGBgIn0= -->\n\n```r\n\npeak.RAD21 <- importPeak(here(refDir, \"33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed\"))\ngroup1 <- fread(here(refDir, \"geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv\"))$gene\ngroup2 <- fread(here(refDir, \"geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv\"))$gene\n\n\n##########################################################\nname <- \"chromo_cons_annoHierarchy\"\n\ngeneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, \"_p-n_ensemblList.tsv\")),\n                                      diffCutoff = 0.2,\n                                      annoList = c(\"P-E\"))\n\ntemp1 <- geneAnnoData %>% dplyr::filter(A1 == \"E\") %>% dplyr::select(chrom1, start1, end1, gene)\ncolnames(temp1) <- c(\"chr\", \"start\", \"end\", \"gene\")\ntemp2 <- geneAnnoData %>% dplyr::filter(A2 == \"E\") %>% dplyr::select(chrom2, start2, end2, gene)\ncolnames(temp2) <- c(\"chr\", \"start\", \"end\", \"gene\")\n\nenhAnchors <- bind_rows(temp1, temp2) %>% unnest(gene) %>%\n  dplyr::mutate(\n    group = case_when(\n      gene %in% group1 ~ \"Grp1\",\n      gene %in% group2 ~ \"Grp2\",\n      TRUE ~ NA\n    )\n  ) %>% dplyr::filter(!is.na(group))\n\nenh <- makeGRangesFromDataFrame(enhAnchors %>% dplyr::select(c(1, 2, 3)))\n\n# Count overlaps between peaks and genes\noverlap_counts <- countOverlaps(enh, peak.RAD21)\n\n# Add overlap counts to the original gene.tb data\nenhAnchors$peak_count <- overlap_counts\n\nenhAnchors <- enhAnchors %>% dplyr::mutate(\n  peakGroup = case_when(\n    peak_count == 0 ~ \"peakNum0\",\n    peak_count == 1 ~ \"peakNum1\",\n    peak_count == 2 ~ \"peakNum2\",\n    peak_count == 3 ~ \"peakNum3\",\n    peak_count >= 4 ~ \"peakNumOver4\"\n  )\n)\n\naaa <- enhAnchors %>% \n  dplyr::select(group, peakGroup, gene) %>%\n  dplyr::mutate(hasPeak = ifelse(peakGroup == \"peakNum0\", FALSE, TRUE))\n\nbbb <- aaa %>% dplyr::filter(group == \"Grp1\") \nsum(bbb$hasPeak)/nrow(bbb)\nbbb <- aaa %>% dplyr::filter(group == \"Grp2\") \nsum(bbb$hasPeak)/nrow(bbb)\n\n\naaa_summary <- aaa %>%\n  group_by(group, peakGroup) %>%\n  summarize(count = n(), .groups = \"drop\")\n\n# Create the stacked barplot\naaa_summary <- aaa %>%\n  group_by(group, peakGroup) %>%\n  summarize(count = n(), .groups = \"drop\") %>%\n  group_by(group) %>%\n  mutate(ratio = count / sum(count))\n\n# Define the gradient colors\n# gradient_colors <- c(\"#D4D4D4\", \"#DCB0AF\", \"#E48D8A\", \"#EC6965\", \"#F44641\")\ngradient_colors <- c(\"#D4D4D4\", \"#A2BBCA\", \"#71A2C0\", \"#3F89B7\", \"#0E71AD\")\n\n# Create the stacked barplot with gradient colors\np <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = peakGroup)) +\n  geom_bar(stat = \"identity\", position = \"stack\",\n           linewidth = lineMedium * mmToLineUnit, lineend = \"square\") +\n  theme_classic() + labs(x = NULL , y = \"Ratio\") +\n  theme(\n    axis.title = element_text(\n      size = fontSizeM, family = fontType, color = \"#000000\"\n    ),\n    axis.text = element_text(\n      size = fontSizeS, family = fontType, color = \"#000000\"\n    ),\n    axis.line = element_line(\n      color = \"#000000\", size = lineThick * mmToLineUnit, lineend = \"square\"\n    ),\n    axis.ticks = element_line(\n      color = \"#000000\", size = lineThick * mmToLineUnit, lineend = \"square\"\n    ),\n    panel.background = element_rect(fill = \"transparent\"),\n    legend.text = element_text(family = fontType, size = fontSizeS),\n    legend.title = element_text(family = fontType, size = fontSizeS)\n  ) +\n  scale_fill_manual(values = gradient_colors, \n                    guide = guide_legend(\n      override.aes = list(size = 0.5), # Adjust legend key symbol size\n      keywidth = 2 / 2.54, # Convert 2mm to cm\n      keyheight = 2 / 2.54 # Convert 2mm to cm\n    )) \n\nwidth <- panelSize(1.7)*mmToInch\nheight <- panelSize(1.3)*mmToInch\nfileName <- paste0(\"rad21perc_enh\")\nsvglite(here(figDir, paste0(fileName, \".svg\")),  height = height, width = width)\nprint(p)\ndev.off()  \n\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
peak.RAD21 <- importPeak(here(refDir, "33250_RAD21_ab992_Bruce-4_peaks.mergePeak.bed"))
group1 <- fread(here(refDir, "geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup1.tsv"))$gene
group2 <- fread(here(refDir, "geneList_dTAG_vs_DMSO_RNA_loop_binaryGroup2.tsv"))$gene
##########################################################
name <- "chromo_cons_annoHierarchy"
geneAnnoData <- loadLoopAnnoData(here(consensusDir, paste0(name, "_p-n_ensemblList.tsv")),
diffCutoff = 0.2,
annoList = c("P-E"))
temp1 <- geneAnnoData %>% dplyr::filter(A1 == "E") %>% dplyr::select(chrom1, start1, end1, gene)
colnames(temp1) <- c("chr", "start", "end", "gene")
temp2 <- geneAnnoData %>% dplyr::filter(A2 == "E") %>% dplyr::select(chrom2, start2, end2, gene)
colnames(temp2) <- c("chr", "start", "end", "gene")
enhAnchors <- bind_rows(temp1, temp2) %>% unnest(gene) %>%
dplyr::mutate(
group = case_when(
gene %in% group1 ~ "Grp1",
gene %in% group2 ~ "Grp2",
TRUE ~ NA
)
) %>% dplyr::filter(!is.na(group))
enh <- makeGRangesFromDataFrame(enhAnchors %>% dplyr::select(c(1, 2, 3)))
# Count overlaps between peaks and genes
overlap_counts <- countOverlaps(enh, peak.RAD21)
# Add overlap counts to the original gene.tb data
enhAnchors$peak_count <- overlap_counts
enhAnchors <- enhAnchors %>% dplyr::mutate(
peakGroup = case_when(
peak_count == 0 ~ "peakNum0",
peak_count == 1 ~ "peakNum1",
peak_count == 2 ~ "peakNum2",
peak_count == 3 ~ "peakNum3",
peak_count >= 4 ~ "peakNumOver4"
)
)
aaa <- enhAnchors %>%
dplyr::select(group, peakGroup, gene) %>%
dplyr::mutate(hasPeak = ifelse(peakGroup == "peakNum0", FALSE, TRUE))
bbb <- aaa %>% dplyr::filter(group == "Grp1")
sum(bbb$hasPeak)/nrow(bbb)
bbb <- aaa %>% dplyr::filter(group == "Grp2")
sum(bbb$hasPeak)/nrow(bbb)
aaa_summary <- aaa %>%
group_by(group, peakGroup) %>%
summarize(count = n(), .groups = "drop")
# Create the stacked barplot
aaa_summary <- aaa %>%
group_by(group, peakGroup) %>%
summarize(count = n(), .groups = "drop") %>%
group_by(group) %>%
mutate(ratio = count / sum(count))
# Define the gradient colors
# gradient_colors <- c("#D4D4D4", "#DCB0AF", "#E48D8A", "#EC6965", "#F44641")
gradient_colors <- c("#D4D4D4", "#A2BBCA", "#71A2C0", "#3F89B7", "#0E71AD")
# Create the stacked barplot with gradient colors
p <- ggplot(aaa_summary, aes(x = group, y = ratio, fill = peakGroup)) +
geom_bar(stat = "identity", position = "stack",
linewidth = lineMedium * mmToLineUnit, lineend = "square") +
theme_classic() + labs(x = NULL , y = "Ratio") +
theme(
axis.title = element_text(
size = fontSizeM, family = fontType, color = "#000000"
),
axis.text = element_text(
size = fontSizeS, family = fontType, color = "#000000"
),
axis.line = element_line(
color = "#000000", size = lineThick * mmToLineUnit, lineend = "square"
),
axis.ticks = element_line(
color = "#000000", size = lineThick * mmToLineUnit, lineend = "square"
),
panel.background = element_rect(fill = "transparent"),
legend.text = element_text(family = fontType, size = fontSizeS),
legend.title = element_text(family = fontType, size = fontSizeS)
) +
scale_fill_manual(values = gradient_colors,
guide = guide_legend(
override.aes = list(size = 0.5), # Adjust legend key symbol size
keywidth = 2 / 2.54, # Convert 2mm to cm
keyheight = 2 / 2.54 # Convert 2mm to cm
))
width <- panelSize(1.7)*mmToInch
height <- panelSize(1.3)*mmToInch
fileName <- paste0("rad21perc_enh")
svglite(here(figDir, paste0(fileName, ".svg")), height = height, width = width)
print(p)
dev.off()
data <- fread(here(resultDir, "chromHMM", "A485_pe-pe_anchors", "overlap_enrich_100_state.txt"))
colnames(data) <- c("state", "genome", "pe-pe_A485_down", "pe-pe_A485_no", "pe-pe_A485_up")
data <- data %>% dplyr::select(-genome) %>% as.matrix()
column_to_rownames(var = "state") %>%
as.matrix()
library(circlize)
col_fun <- colorRamp2(c(0, 1, 10),
c("blue", "white", "red"))
library(circlize)
col_fun <- colorRamp2(c(min(data), max(data)), c("white", "red"))
#fviz_nbclust(data, kmeans, method = "wss")
p <- Heatmap(
data,
name = "Odds Ratio", # Name of the heatmap legend
cluster_columns = FALSE, # Remove column dendrogram
row_km = 10, # Define the number of k-means clusters for rows (adjust as needed)
show_row_dend = FALSE,
col = col_fun,
border = TRUE
)
fileName <- paste0("anchorLOLA_dTAG_vs_DMSO_diff0.2_allLoops_extreme_regAnchorBackground_atac")
height <- 7
width <- 3.5
png(here(figDir, paste0(fileName, ".png")), res = 600, unit = "in", height = height, width = width)
print(p)
dev.off()
svglite(here(figDir, paste0(fileName, ".svg")), height = height, width = width)
print(p)
dev.off()